| 1. |
- Aarno, Daniel, et al.
(författare)
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Adaptive virtual fixtures for machine-assisted teleoperation tasks
- 2005
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Ingår i: 2005 IEEE International Conference on Robotics and Automation (ICRA), Vols 1-4. - 078038914X ; , s. 1139-1144
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Konferensbidrag (refereegranskat)abstract
- It has been demonstrated in a number of robotic areas how the use of virtual fixtures improves task performance both in terms of execution time and overall precision, [1]. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we propose the use of adaptive virtual fixtures that enable us to cope with the above problems. A teleoperative or human machine collaborative setting is assumed with the core idea of dividing the task, that the operator is executing, into several subtasks. The operator may remain in each of these subtasks as long as necessary and switch freely between them. Hence, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. In our system, the probability that the user is following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance. Thus, an on-line decision of how to fixture the movement is provided.
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| 2. |
- Aarno, Daniel, et al.
(författare)
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Artificial potential biased probabilistic roadmap method
- 2004
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Ingår i: 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS. - 0780382323 ; , s. 461-466
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Konferensbidrag (refereegranskat)abstract
- Probabilistic roadmap methods (PRMs) have been successfully used to solve difficult path planning problems but their efficiency is limited when the free space contains narrow passages through which the robot must pass. This paper presents a new sampling scheme that aims to increase the probability of finding paths through narrow passages. Here, a biased sampling scheme is used to increase the distribution of nodes in narrow regions of the free space. A partial computation of the artificial potential field is used to bias the distribution of nodes.
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| 3. |
- Aarno, Daniel, et al.
(författare)
-
Constrained path planning and task-consistent path adaptation for mobile manipulators
- 2005
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Ingår i: 2005 12th International Conference on Advanced Robotics. - 0780391772 ; , s. 268-273
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Konferensbidrag (refereegranskat)abstract
- This paper presents our ongoing research in the design of a versatile service robot capable of operating in a home or office environment. Ideas presented here cover architectural issues and possible applications for such a robot system with focus on tasks requiring constrained end-effector motions. Two key components of such system is a path planner and a reactive behavior capable of force relaxation and path adaptation. These components are presented in detail along with an overview of the software architecture they fit into.
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| 4. |
- Aarno, Daniel, et al.
(författare)
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Early reactive grasping with second order 3D feature relations
- 2008
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Ingår i: Recent Progress In Robotics: Viable Robotic Service To Human. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783540767282 ; , s. 91-105
-
Konferensbidrag (refereegranskat)abstract
- One of the main challenges in the field of robotics is to make robots ubiquitous. To intelligently interact with the world, such robots need to understand the environment and situations around them and react appropriately, they need context-awareness. But how to equip robots with capabilities of gathering and interpreting the necessary information for novel tasks through interaction with the environment and by providing some minimal knowledge in advance? This has been a longterm question and one of the main drives in the field of cognitive system development. The main idea behind the work presented in this paper is that the robot should, like a human infant, learn about objects by interacting with them, forming representations of the objects and their categories that are grounded in its embodiment. For this purpose, we study an early learning of object grasping process where the agent, based on a set of innate reflexes and knowledge about its embodiment. We stress out that this is not the work on grasping, it is a system that interacts with the environment based on relations of 3D visual features generated trough a stereo vision system. We show how geometry, appearance and spatial relations between the features can guide early reactive grasping which can later on be used in a more purposive manner when interacting with the environment.
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| 5. |
- Aarno, Daniel, 1979-
(författare)
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Intention recognition in human machine collaborative systems
- 2007
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Robotsystem har använts flitigt under de senaste årtiondena för att skapa automationslösningar i ett flertal områden. De flesta nuvarande automationslösningarna är begränsade av att uppgifterna de kan lösa måste vara repetitiva och förutsägbara. En av anledningarna till detta är att dagens robotsystem saknar förmåga att förstå och resonera om omvärlden. På grund av detta har forskare inom robotik och artificiell intelligens försökt att skapa intelligentare maskiner. Trots att stora framsteg har gjorts då det gäller att skapa robotar som kan fungera och interagera i en mänsklig miljö så finns det för nuvarande inget system som kommer i närheten av den mänskliga förmågan att resonera om omvärlden. För att förenkla problemet har vissa forskare föreslagit en alternativ lösning till helt självständiga robotar som verkar i mänskliga miljöer. Alternativet är att kombinera människors och maskiners förmågor. Exempelvis så kan en person verka på en avlägsen plats, som kanske inte är tillgänglig för personen i fråga på grund av olika orsaker, genom att använda fjärrstyrning. Vid fjärrstyrning skickar operatören kommandon till en robot som verkar som en förlängning av operatörens egen kropp. Segmentering och identifiering av rörelser skapade av en operatör kan användas för att tillhandahålla korrekt assistans vid fjärrstyrning eller samarbete mellan människa och maskin. Assistansen sker ofta inom ramen för virtuella fixturer där eftergivenheten hos fixturen kan justeras under exekveringen för att tillhandahålla ökad prestanda i form av ökad precision och minskad tid för att utföra uppgiften. Den här avhandlingen fokuserar på två aspekter av samarbete mellan människa och maskin. Klassificering av en operatörs rörelser till ett på förhand specificerat tillstånd under en manipuleringsuppgift och assistans under manipuleringsuppgiften baserat på virtuella fixturer. Den specifika tillämpningen som behandlas är manipuleringsuppgifter där en mänsklig operatör styr en robotmanipulator i ett fjärrstyrt eller samarbetande system. En metod för att följa förloppet av en uppgift medan den utförs genom att använda virtuella fixturer presenteras. Istället för att följa en på förhand specificerad plan så har operatören möjlighet att undvika oväntade hinder och avvika från modellen. För att möjliggöra detta estimeras kontinuerligt sannolikheten att operatören följer en viss trajektorie (deluppgift). Estimatet används sedan för att justera eftergivenheten hos den virtuella fixturen så att ett beslut om hur rörelsen ska fixeras kan tas medan uppgiften utförs. En flerlagers dold Markovmodell (eng. layered hidden Markov model) används för att modellera mänskliga färdigheter. En gestemklassificerare som klassificerar en operatörs rörelser till olika grundläggande handlingsprimitiver, eller gestemer, evalueras. Gestemklassificerarna används sedan i en flerlagers dold Markovmodell för att modellera en simulerad fjärrstyrd manipuleringsuppgift. Klassificeringsprestandan utvärderas med avseende på brus, antalet gestemer, typen på den dolda Markovmodellen och antalet tillgängliga träningssekvenser. Den flerlagers dolda Markovmodellen tillämpas sedan på data från en trajektorieföljningsuppgift i 2D och 3D med en robotmanipulator för att ge både kvalitativa och kvantitativa resultat. Resultaten tyder på att den flerlagers dolda Markovmodellen är väl lämpad för att modellera trajektorieföljningsuppgifter och att den flerlagers dolda Markovmodellen är robust med avseende på felklassificeringar i de underliggande gestemklassificerarna.
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| 6. |
- Aarno, Daniel, et al.
(författare)
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Layered HMM for motion intention recognition
- 2006
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Ingår i: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12. - NEW YORK : IEEE. - 9781424402588 ; , s. 5130-5135
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Konferensbidrag (refereegranskat)abstract
- Acquiring, representing and modeling human skins is one of the key research areas in teleoperation, programming. by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtasks in order to provide manageable modeling. In this paper we consider the use of a Layered Hidden Markov Model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classilication performance with respect to noise, number of gestems, type of HAIM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi dimensional HMMs for gestem classification is small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers.
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| 7. |
- Aarno, Daniel, et al.
(författare)
-
Motion intention recognition in robot assisted applications
- 2008
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Ingår i: Robotics and Autonomous Systems. - : Elsevier BV. - 0921-8890 .- 1872-793X. ; 56:8, s. 692-705
-
Tidskriftsartikel (refereegranskat)abstract
- Acquiring, representing and modelling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. The problems are challenging mainly because of the lack of a general mathematical model to describe human skills. One of the common approaches is to divide the task that the operator is executing into several subtasks or low-level subsystems in order to provide manageable modelling. In this paper we consider the use of a Layered Hidden Markov Model (LHMM) to model human skills. We evaluate a gesteme classifier that classifies motions into basic action-primitives, or gestemes. The gesteme classifiers are then used in a LHMM to model a teleoperated task. The proposed methodology uses three different HMM models at the gesteme level: one-dimensional HMM, multi-dimensional HMM and multidimensional HMM with Fourier transform. The online and off-line classification performance of these three models is evaluated with respect to the number of gestemes, the influence of the number of training samples, the effect of noise and the effect of the number of observation symbols. We also apply the LHMM to data recorded during the execution of a trajectory tracking task in 2D and 3D with a mobile manipulator in order to provide qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modelling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multidimensional HMMs for gesteme classification is small. It can also be seen that the LHMM is robust with respect to misclassifications in the underlying gesteme classifiers.
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| 8. |
- Abdul Khader, Shahbaz
(författare)
-
Data-Driven Methods for Contact-Rich Manipulation: Control Stability and Data-Efficiency
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Autonomous robots are expected to make a greater presence in the homes and workplaces of human beings. Unlike their industrial counterparts, autonomous robots have to deal with a great deal of uncertainty and lack of structure in their environment. A remarkable aspect of performing manipulation in such a scenario is the possibility of physical contact between the robot and the environment. Therefore, not unlike human manipulation, robotic manipulation has to manage contacts, both expected and unexpected, that are often characterized by complex interaction dynamics.Skill learning has emerged as a promising approach for robots to acquire rich motion generation capabilities. In skill learning, data driven methods are used to learn reactive control policies that map states to actions. Such an approach is appealing because a sufficiently expressive policy can almost instantaneously generate appropriate control actions without the need for computationally expensive search operations. Although reinforcement learning (RL) is a natural framework for skill learning, its practical application is limited for a number of reasons. Arguably, the two main reasons are the lack of guaranteed control stability and poor data-efficiency. While control stability is necessary for ensuring safety and predictability, data-efficiency is required for achieving realistic training times. In this thesis, solutions are sought for these two issues in the context of contact-rich manipulation.First, this thesis addresses the problem of control stability. Despite unknown interaction dynamics during contact, skill learning with stability guarantee is formulated as a model-free RL problem. The thesis proposes multiple solutions for parameterizing stability-aware policies. Some policy parameterizations are partly or almost wholly deep neural networks. This is followed by policy search solutions that preserve stability during random exploration, if required. In one case, a novel evolution strategies-based policy search method is introduced. It is shown, with the help of real robot experiments, that Lyapunov stability is both possible and beneficial for RL-based skill learning.Second, this thesis addresses the issue of data-efficiency. Although data-efficiency is targeted by formulating skill learning as a model-based RL problem, only the model learning part is addressed. In addition to benefiting from the data-efficiency and uncertainty representation of the Gaussian process, this thesis further investigates the benefits of adopting the structure of hybrid automata for learning forward dynamics models. The method also includes an algorithm for predicting long-term trajectory distributions that can represent discontinuities and multiple modes. The proposed method is shown to be more data-efficient than some state-of-the-art methods.
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| 9. |
- Abdul Khader, Shahbaz, et al.
(författare)
-
Data-Efficient Model Learning and Prediction for Contact-Rich Manipulation Tasks
- 2020
-
Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 5:3, s. 4321-4328
-
Tidskriftsartikel (refereegranskat)abstract
- In this letter, we investigate learning forward dynamics models and multi-step prediction of state variables (long-term prediction) for contact-rich manipulation. The problems are formulated in the context of model-based reinforcement learning (MBRL). We focus on two aspects-discontinuous dynamics and data-efficiency-both of which are important in the identified scope and pose significant challenges to State-of-the-Art methods. We contribute to closing this gap by proposing a method that explicitly adopts a specific hybrid structure for the model while leveraging the uncertainty representation and data-efficiency of Gaussian process. Our experiments on an illustrative moving block task and a 7-DOF robot demonstrate a clear advantage when compared to popular baselines in low data regimes.
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| 10. |
- Abdul Khader, Shahbaz, et al.
(författare)
-
Learning deep energy shaping policies for stability-guaranteed manipulation
- 2021
-
Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 6:4, s. 8583-8590
-
Tidskriftsartikel (refereegranskat)abstract
- Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability whenever a control law is synthesized. What makes traditional stability analysis difficult for DRL are the uninterpretable nature of the neural network policies and unknown system dynamics. In this work, stability is obtained by deriving an interpretable deep policy structure based on the energy shaping control of Lagrangian systems. Then, stability during physical interaction with an unknown environment is established based on passivity. The result is a stability guaranteeing DRL in a model-free framework that is general enough for contact-rich manipulation tasks. With an experiment on a peg-in-hole task, we demonstrate, to the best of our knowledge, the first DRL with stability guarantee on a real robotic manipulator.
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| 11. |
- Abdul Khader, Shahbaz, et al.
(författare)
-
Learning Deep Neural Policies with Stability Guarantees
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability whenever a control law is synthesized. What makes traditional stability analysis difficult for DRL are the uninterpretable nature of the neural network policies and unknown system dynamics. In this work, unconditional stability is obtained by deriving an interpretable deep policy structure based on the energy shaping control of Lagrangian systems. Then, stability during physical interaction with an unknown environment is established based on passivity. The result is a stability guaranteeing DRL in a model-free framework that is general enough for contact-rich manipulation tasks. With an experiment on a peg-in-hole task, we demonstrate, to the best of our knowledge, the first DRL with stability guarantee on a real robotic manipulator.
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| 12. |
- Abdul Khader, Shahbaz, et al.
(författare)
-
Learning Stable Normalizing-Flow Control for Robotic Manipulation
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Reinforcement Learning (RL) of robotic manipu-lation skills, despite its impressive successes, stands to benefitfrom incorporating domain knowledge from control theory. Oneof the most important properties that is of interest is controlstability. Ideally, one would like to achieve stability guaranteeswhile staying within the framework of state-of-the-art deepRL algorithms. Such a solution does not exist in general,especially one that scales to complex manipulation tasks. Wecontribute towards closing this gap by introducing normalizing-flow control structure, that can be deployed in any latest deepRL algorithms. While stable exploration is not guaranteed,our method is designed to ultimately produce deterministiccontrollers with provable stability. In addition to demonstratingour method on challenging contact-rich manipulation tasks, wealso show that it is possible to achieve considerable explorationefficiency–reduced state space coverage and actuation efforts–without losing learning efficiency.
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| 13. |
- Abdul Khader, Shahbaz, et al.
(författare)
-
Learning Stable Normalizing-Flow Control for Robotic Manipulation
- 2021
-
Ingår i: 2021 IEEE International Conference On Robotics And Automation (ICRA 2021). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1644-1650
-
Konferensbidrag (refereegranskat)abstract
- Reinforcement Learning (RL) of robotic manipulation skills, despite its impressive successes, stands to benefit from incorporating domain knowledge from control theory. One of the most important properties that is of interest is control stability. Ideally, one would like to achieve stability guarantees while staying within the framework of state-of-the-art deep RL algorithms. Such a solution does not exist in general, especially one that scales to complex manipulation tasks. We contribute towards closing this gap by introducing normalizing-flow control structure, that can be deployed in any latest deep RL algorithms. While stable exploration is not guaranteed, our method is designed to ultimately produce deterministic controllers with provable stability. In addition to demonstrating our method on challenging contact-rich manipulation tasks, we also show that it is possible to achieve considerable exploration efficiency-reduced state space coverage and actuation efforts- without losing learning efficiency.
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| 14. |
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| 15. |
- Ahlberg, Sofie, et al.
(författare)
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Co-adaptive Human-Robot Cooperation : Summary and Challenges
- 2022
-
Ingår i: Unmanned Systems. - : World Scientific Pub Co Pte Ltd. - 2301-3850 .- 2301-3869. ; 10:02, s. 187-203
-
Tidskriftsartikel (refereegranskat)abstract
- The work presented here is a culmination of developments within the Swedish project COIN: Co-adaptive human-robot interactive systems, funded by the Swedish Foundation for Strategic Research (SSF), which addresses a unified framework for co-adaptive methodologies in human-robot co-existence. We investigate co-adaptation in the context of safe planning/control, trust, and multi-modal human-robot interactions, and present novel methods that allow humans and robots to adapt to one another and discuss directions for future work.
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| 16. |
- Almeida, Diogo, 1991-, et al.
(författare)
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Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2017
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Ingår i: Warehouse Picking Automation Workshop 2017.
-
Konferensbidrag (populärvet., debatt m.m.)abstract
- In this work we summarize the solution developed by Team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition simulated a warehouse automation scenario and it was divided in two tasks: a picking task where a robot picks items from a shelf and places them in a tote and a stowing task which is the inverse task where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting from a high level overview of our system and later delving into details of our perception pipeline and our strategy for manipulation and grasping. The solution was implemented using a Baxter robot equipped with additional sensors.
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| 17. |
- Almeida, Diogo, 1991-, et al.
(författare)
-
Team KTH’s Picking Solution for the Amazon Picking Challenge 2016
- 2020
-
Ingår i: Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment. - Cham : Springer Nature. ; , s. 53-62
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter we summarize the solution developed by team KTH for the Amazon Picking Challenge 2016 in Leipzig, Germany. The competition, which simulated a warehouse automation scenario, was divided into two parts: a picking task, where the robot picks items from a shelf and places them into a tote, and a stowing task, where the robot picks items from a tote and places them in a shelf. We describe our approach to the problem starting with a high-level overview of the system, delving later into the details of our perception pipeline and strategy for manipulation and grasping. The hardware platform used in our solution consists of a Baxter robot equipped with multiple vision sensors.
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| 18. |
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| 19. |
- Antonova, Rika, et al.
(författare)
-
Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We address the problem of learning reusable state representations from streaming high-dimensional observations. This is important for areas like Reinforcement Learning (RL), which yields non-stationary data distributions during training. We make two key contributions. First, we propose an evaluation suite that measures alignment between latent and true low-dimensional states. We benchmark several widely used unsupervised learning approaches. This uncovers the strengths and limitations of existing approaches that impose additional constraints/objectives on the latent space. Our second contribution is a unifying mathematical formulation for learning latent relations. We learn analytic relations on source domains, then use these relations to help structure the latent space when learning on target domains. This formulation enables a more general, flexible and principled way of shaping the latent space. It formalizes the notion of learning independent relations, without imposing restrictive simplifying assumptions or requiring domain-specific information. We present mathematical properties, concrete algorithms for implementation and experimental validation of successful learning and transfer of latent relations.
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| 20. |
- Antonova, Rika, et al.
(författare)
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Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
- 2019
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Ingår i: Proceedings of the Conference on Robot Learning, CoRL 2019. - : ML Research Press. ; , s. 456-465
-
Konferensbidrag (refereegranskat)abstract
- Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work, we focus on robotics problems with a budget of only 10-20 trials. This is a very challenging setting even for data-efficient approaches like Bayesian optimization (BO), especially when optimizing higher-dimensional controllers. Previous work extracted expert-designed low-dimensional features from simulation trajectories to construct informed kernels and run ultra sample-efficient BO on hardware. We remove the need for expert-designed features by proposing a model and architecture for a sequential variational autoencoder that embeds the space of simulated trajectories into a lower-dimensional space of latent paths in an unsupervised way. We further compress the search space for BO by reducing exploration in parts of the state space that are undesirable, without requiring explicit constraints on controller parameters. We validate our approach with hardware experiments on a Daisy hexapod robot and an ABB Yumi manipulator. We also present simulation experiments with further comparisons to several baselines on Daisy and two manipulators. Our experiments indicate the proposed trajectory-based kernel with dynamic compression can offer ultra data-efficient optimization.
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| 21. |
- Antonova, Rika, et al.
(författare)
-
Bayesian optimization in variational latent spaces with dynamic compression
- 2020
-
Ingår i: Proceedings of Machine Learning Research. ; , s. 456-465
-
Konferensbidrag (refereegranskat)abstract
- Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work, we focus on robotics problems with a budget of only 10-20 trials. This is a very challenging setting even for data- efficient approaches like Bayesian optimization (BO), especially when optimizing higher-dimensional controllers. Previous work extracted expert-designed low-dimensional features from simulation trajectories to construct informed kernels and run ultra sample-efficient BO on hardware. We remove the need for expert-designed features by proposing a model and architecture for a sequential variational autoencoder that embeds the space of simulated trajectories into a lower-dimensional space of latent paths in an unsupervised way. We further compress the search space for BO by reducing exploration in parts of the state space that are undesirable, without requiring explicit constraints on controller parameters. We validate our approach with hardware experiments on a Daisy hexapod robot and an ABB Yumi manipulator. We also present simulation experiments with further comparisons to several baselines on Daisy and two manipulators. Our experiments indicate the proposed trajectory-based kernel with dynamic compression can offer ultra data-efficient optimization.
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| 22. |
- Antonova, Rika, et al.
(författare)
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Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
- 2018
-
Ingår i: Proceedings of Machine Learning Research. - : PMLR. ; , s. 641-650, s. 641-650
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Konferensbidrag (refereegranskat)abstract
- We develop an approach that benefits from large simulated datasets and takes full advantage of the limited online data that is most relevant. We propose a variant of Bayesian optimization that alternates between using informed and uninformed kernels. With this Bernoulli Alternation Kernel we ensure that discrepancies between simulation and reality do not hinder adapting robot control policies online. The proposed approach is applied to a challenging real-world problem of task-oriented grasping with novel objects. Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores. We learn task scores from a labeled dataset with a convolutional network, which is used to construct an informed kernel for our variant of Bayesian optimization. Experiments on an ABB Yumi robot with real sensor data demonstrate success of our approach, despite the challenge of fulfilling task requirements and high uncertainty over physical properties of objects.
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| 23. |
- Antonova, Rika, et al.
(författare)
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How to Sim2Real with Gaussian Processes: Prior Mean versus Kernels as Priors
- 2020
-
Ingår i: 2nd Workshop on Closing the Reality Gap in Sim2Real Transfer for Robotics. RSS, 2020. https://sim2real.github.io.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Gaussian Processes (GPs) have been widely used in robotics as models, and more recently as key structures in active learning algorithms, such as Bayesian optimization. GPs consist of two main components: the mean function and the kernel. Specifying a prior mean function has been a common way to incorporate prior knowledge. When a prior mean function could not be constructed manually, the next default has been to incorporate prior (simulated) observations into a GP as 'fake' data. Then, this GP would be used to further learn from true data on the target (real) domain. We argue that embedding prior knowledge into GP kernels instead provides a more flexible way to capture simulation-based information. We give examples of recent works that demonstrate the wide applicability of such kernel-centric treatment when using GPs as part of Bayesian optimization. We also provide discussion that helps to build intuition for why such 'kernels as priors' view is beneficial.
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| 24. |
- Antonova, Rika, et al.
(författare)
-
Reinforcement Learning for Pivoting Task
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this work we propose an approach to learn a robust policy for solving the pivoting task. Recently, several model-free continuous control algorithms were shown to learn successful policies without prior knowledge of the dynamics of the task. However, obtaining successful policies required thousands to millions of training episodes, limiting the applicability of these approaches to real hardware. We developed a training procedure that allows us to use a simple custom simulator to learn policies robust to the mismatch of simulation vs robot. In our experiments, we demonstrate that the policy learned in the simulator is able to pivot the object to the desired target angle on the real robot. We also show generalization to an object with different inertia, shape, mass and friction properties than those used during training. This result is a step towards making model-free reinforcement learning available for solving robotics tasks via pre-training in simulators that offer only an imprecise match to the real-world dynamics.
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| 25. |
- Antonova, Rika, et al.
(författare)
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Sequential Topological Representations for Predictive Models of Deformable Objects
- 2021
-
Ingår i: Proceedings of the 3rd Conference on Learning for Dynamics and Control, L4DC 2021. - : ML Research Press. ; , s. 348-360
-
Konferensbidrag (refereegranskat)abstract
- Deformable objects present a formidable challenge for robotic manipulation due to the lack of canonical low-dimensional representations and the difficulty of capturing, predicting, and controlling such objects. We construct compact topological representations to capture the state of highly deformable objects that are topologically nontrivial. We develop an approach that tracks the evolution of this topological state through time. Under several mild assumptions, we prove that the topology of the scene and its evolution can be recovered from point clouds representing the scene. Our further contribution is a method to learn predictive models that take a sequence of past point cloud observations as input and predict a sequence of topological states, conditioned on target/future control actions. Our experiments with highly deformable objects in simulation show that the proposed multistep predictive models yield more precise results than those obtained from computational topology libraries. These models can leverage patterns inferred across various objects and offer fast multistep predictions suitable for real-time applications.
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| 26. |
- Antonova, Rika
(författare)
-
Transfer-Aware Kernels, Priors and Latent Spaces from Simulation to Real Robots
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Consider challenging sim-to-real cases lacking high-fidelity simulators and allowing only 10-20 hardware trials. This work shows that even imprecise simulation can be beneficial if used to build transfer-aware representations.First, the thesis introduces an informed kernel that embeds the space of simulated trajectories into a lower-dimensional space of latent paths. It uses a sequential variational autoencoder (sVAE) to handle large-scale training from simulated data. Its modular design enables quick adaptation when used for Bayesian optimization (BO) on hardware. The thesis and the included publications demonstrate that this approach works for different areas of robotics: locomotion and manipulation. Furthermore, a variant of BO that ensures recovery from negative transfer when using corrupted kernels is introduced. An application to task-oriented grasping validates its performance on hardware.For the case of parametric learning, simulators can serve as priors or regularizers. This work describes how to use simulation to regularize a VAE's decoder to bind the VAE's latent space to simulator parameter posterior. With that, training on a small number of real trajectories can quickly shift the posterior to reflect reality. The included publication demonstrates that this approach can also help reinforcement learning (RL) quickly overcome the sim-to-real gap on a manipulation task on hardware.A longer-term vision is to shape latent spaces without needing to mandate a particular simulation scenario. A first step is to learn general relations that hold on sequences of states from a set of related domains. This work introduces a unifying mathematical formulation for learning independent analytic relations. Relations are learned from source domains, then used to help structure the latent space when learning on target domains. This formulation enables a more general, flexible and principled way of shaping the latent space. It formalizes the notion of learning independent relations, without imposing restrictive simplifying assumptions or requiring domain-specific information. This work presents mathematical properties, concrete algorithms and experimental validation of successful learning and transfer of latent relations.
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| 27. |
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| 28. |
- Arnekvist, Isac, 1986-
(författare)
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Transfer Learning using low-dimensional Representations in Reinforcement Learning
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Successful learning of behaviors in Reinforcement Learning (RL) are often learned tabula rasa, requiring many observations and interactions in the environment. Performing this outside of a simulator, in the real world, often becomes infeasible due to the large amount of interactions needed. This has motivated the use of Transfer Learning for Reinforcement Learning, where learning is accelerated by using experiences from previous learning in related tasks. In this thesis, I explore how we can transfer from a simple single-object pushing policy, to a wide array of non-prehensile rearrangement problems. I then explain how we can model task differences using a low-dimensional latent variable representation to make adaption to novel tasks efficient. Lastly, the dependence of accurate function approximation is sometimes problematic, especially in RL, where statistics of target variables are not known a priori. I present observations, along with explanations, that small target variances along with momentum optimization of ReLU-activated neural network parameters leads to dying ReLU.
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| 29. |
- Arnekvist, Isac, 1986-, et al.
(författare)
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Vpe : Variational policy embedding for transfer reinforcement learning
- 2019
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Ingår i: 2019 International Conference on Robotics And Automation (ICRA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538660263 - 9781538660270 ; , s. 36-42
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Konferensbidrag (refereegranskat)abstract
- Reinforcement Learning methods are capable of solving complex problems, but resulting policies might perform poorly in environments that are even slightly different. In robotics especially, training and deployment conditions often vary and data collection is expensive, making retraining undesirable. Simulation training allows for feasible training times, but on the other hand suffer from a reality-gap when applied in real-world settings. This raises the need of efficient adaptation of policies acting in new environments. We consider the problem of transferring knowledge within a family of similar Markov decision processes. We assume that Q-functions are generated by some low-dimensional latent variable. Given such a Q-function, we can find a master policy that can adapt given different values of this latent variable. Our method learns both the generative mapping and an approximate posterior of the latent variables, enabling identification of policies for new tasks by searching only in the latent space, rather than the space of all policies. The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments. We demonstrate the method on both a pendulum swing-up task in simulation, and for simulation-to-real transfer on a pushing task.
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| 30. |
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| 31. |
- Arriola-Rios, Veronica E., et al.
(författare)
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Modeling of Deformable Objects for Robotic Manipulation : A Tutorial and Review
- 2020
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Ingår i: Frontiers in Robotics and AI. - : Frontiers Media S.A.. - 2296-9144. ; 7
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Forskningsöversikt (refereegranskat)abstract
- Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.
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| 32. |
- Baisero, Andrea, et al.
(författare)
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The path kernel : A novel kernel for sequential data
- 2015
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Ingår i: Pattern Recognition. - Cham : Springer Berlin/Heidelberg. - 9783319126098 ; , s. 71-84
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Konferensbidrag (refereegranskat)abstract
- We define a novel kernel function for finite sequences of arbitrary length which we call the path kernel. We evaluate this kernel in a classification scenario using synthetic data sequences and show that our kernel can outperform state of the art sequential similarity measures. Furthermore, we find that, in our experiments, a clustering of data based on the path kernel results in much improved interpretability of such clusters compared to alternative approaches such as dynamic time warping or the global alignment kernel.
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| 33. |
- Baisero, Andrea, et al.
(författare)
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The Path Kernel
- 2013
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Ingår i: ICPRAM 2013 - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods. - 9789898565419 ; , s. 50-57
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Konferensbidrag (refereegranskat)abstract
- Kernel methods have been used very successfully to classify data in various application domains. Traditionally, kernels have been constructed mainly for vectorial data defined on a specific vector space. Much less work has been addressing the development of kernel functions for non-vectorial data. In this paper, we present a new kernel for encoding sequential data. We present our results comparing the proposed kernel to the state of the art, showing a significant improvement in classification and a much improved robustness and interpretability.
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| 34. |
- Barck-Holst, Carl, et al.
(författare)
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Learning Grasping Affordance Using Probabilistic and Ontological Approaches
- 2009
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Ingår i: 2009 International Conference on Advanced Robotics, ICAR 2009. - : IEEE. - 9781424448555 ; , s. 96-101
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Konferensbidrag (refereegranskat)abstract
- We present two approaches to modeling affordance relations between objects, actions and effects. The first approach we present focuses on a probabilistic approach which uses a voting function to learn which objects afford which types of grasps. We compare the success rate of this approach to a second approach which uses an ontological reasoning engine for learning affordances. Our second approach employs a rule-based system with axioms to reason on grasp selection for a given object.
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| 35. |
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| 36. |
- Bekiroglu, Yasemin, et al.
(författare)
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A probabilistic framework for task-oriented grasp stability assessment
- 2013
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Ingår i: 2013 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE Computer Society. - 1050-4729. - 9781467356411 ; , s. 3040-3047
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Konferensbidrag (refereegranskat)abstract
- We present a probabilistic framework for grasp modeling and stability assessment. The framework facilitates assessment of grasp success in a goal-oriented way, taking into account both geometric constraints for task affordances and stability requirements specific for a task. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot's self-exploration. The conditional relations between tasks and multiple sensory streams (vision, proprioception and tactile) are modeled using Bayesian networks. The generative modeling approach both allows prediction of grasp success, and provides insights into dependencies between variables and features relevant for object grasping.
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| 37. |
- Bekiroglu, Yasemin, et al.
(författare)
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Assessing Grasp Stability Based on Learning and Haptic Data
- 2011
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Ingår i: IEEE Transactions on robotics. - : IEEE Robotics and Automation Society. - 1552-3098 .- 1941-0468. ; 27:3, s. 616-629
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Tidskriftsartikel (refereegranskat)abstract
- An important ability of a robot that interacts with the environment and manipulates objects is to deal with the uncertainty in sensory data. Sensory information is necessary to, for example, perform online assessment of grasp stability. We present methods to assess grasp stability based on haptic data and machinelearning methods, including AdaBoost, support vector machines (SVMs), and hidden Markov models (HMMs). In particular, we study the effect of different sensory streams to grasp stability. This includes object information such as shape; grasp information such as approach vector; tactile measurements fromfingertips; and joint configuration of the hand. Sensory knowledge affects the success of the grasping process both in the planning stage (before a grasp is executed) and during the execution of the grasp (closed-loop online control). In this paper, we study both of these aspects. We propose a probabilistic learning framework to assess grasp stability and demonstrate that knowledge about grasp stability can be inferred using information from tactile sensors. Experiments on both simulated and real data are shown. The results indicate that the idea to exploit the learning approach is applicable in realistic scenarios, which opens a number of interesting venues for the future research.
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| 38. |
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| 39. |
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| 40. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
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Grasp Stability from Vision and Touch
- 2012
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We study the exploitation of tactile and visual feedback to predict the stability of a grasp before attempting to lift an object and thus to prevent failures. Our robot learns an empirical representation of stable and unstable grasps by exploring given grasping configurations.
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| 41. |
- Bekiroglu, Yasemin, et al.
(författare)
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Integrating Grasp Planning with Online Stability Assessment using Tactile Sensing
- 2011
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Ingår i: IEEE International Conference on Robotics and Automation. - : IEEE conference proceedings. - 1050-4729. - 9781612843865 ; , s. 4750-4755
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Konferensbidrag (refereegranskat)abstract
- This paper presents an integration of grasp planning and online grasp stability assessment based on tactile data. We show how the uncertainty in grasp execution posterior to grasp planning can be dealt with using tactile sensing and machine learning techniques. The majority of the state-of-the-art grasp planners demonstrate impressive results in simulation. However, these results are mostly based on perfect scene/object knowledge allowing for analytical measures to be employed. It is questionable how well these measures can be used in realistic scenarios where the information about the object and robot hand may be incomplete and/or uncertain. Thus, tactile and force-torque sensory information is necessary for successful online grasp stability assessment. We show how a grasp planner can be integrated with a probabilistic technique for grasp stability assessment in order to improve the hypotheses about suitable grasps on different types of objects. Experimental evaluation with a three-fingered robot hand equipped with tactile array sensors shows the feasibility and strength of the integrated approach.
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| 42. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
-
Joint Observation of Object Pose and Tactile Imprints for Online Grasp Stability Assessment
- 2011
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper studies the viability of concurrent object pose tracking and tactile sensing for assessing grasp stability on a physical robotic platform. We present a kernel logistic regression model of pose- and touch-conditional grasp success probability. Models are trained on grasp data which consist of (1) the pose of the gripper relative to the object, (2) a tactile description of the contacts between the object and the fully-closed gripper, and (3) a binary description of grasp feasibility, which indicates whether the grasp can be used to rigidly control the object. The data is collected by executing grasps demonstrated by a human on a robotic platform composed of an industrial arm, a three-finger gripper equipped with tactile sensing arrays, and a vision-based object pose tracking system. The robot is able to track the pose of an object while it is grasping it, and it can acquire grasp tactile imprints via pressure sensor arrays mounted on its gripper’s fingers. We consider models defined on several subspaces of our input data – using tactile perceptions or gripper poses only. Models are optimized and evaluated with f-fold cross-validation. Our preliminary results show that stability assessments based on both tactile and pose data can provide better rates than assessments based on tactile data alone.
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| 43. |
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| 44. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
-
Learning grasp stability based on haptic data
- 2010
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Grasping is an essential skill for a general purpose service robot, working in an industrial or home-like environment. The classical work in robotic grasping assumes that the object parameters such as pose, shape, weight and material properties are known. If precise knowledge of these is available, grasp planning using analytical approaches, such as form or force closure, may be enough for successful grasp execution. However, in unstructured environments the information is usually uncertain, which presents a great challenge for the current state-of-the-art work in this area. Sensors can be used to alleviate the problem of uncertainty. To determine the shape and pose of an object, vision has been commonly used. However, the accuracy of vision is limited and small errors in object pose are frequent even for known objects. It is not uncommon that even these small errors cause failures in grasping. These failures are also difficult to prevent at the grasp planning stage. This problem is magnified when also the object models are acquired on-line using vision or other similar sensors. While the tactile and finger force sensors can be used to reduce this problem, a grasp may fail even when all fingers have adequate contact forces and the hand pose is not dramatically different from the planned one. The main contribution of this paper is to show that it is possible to infer knowledge about grasp stability using information from tactile sensors while grasping an object before being further manipulated. This is very useful, because if failures can be detected, objects can be regrasped before trying to lift them. However, the relationship between tactile measurements and grasp stability is embodiment specific and very complex. For this reason, we propose to use machine earning techniques for the inference.
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| 45. |
- Bekiroglu, Yasemin, et al.
(författare)
-
Learning grasp stability based on tactile data and HMMs
- 2010
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Konferensbidrag (refereegranskat)abstract
- In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability based on learning from examples. The main objective is classification of a grasp as stable or unstable before applying further actions on it, e.g. lifting. The problem cannot be solved by visual sensing which is typically used to execute an initial robot hand positioning with respect to the object. The output of the classification system can trigger a regrasping step if an unstable grasp is identified. An off-line learning process is implemented and used for reasoning about grasp stability for a three-fingered robotic hand using Hidden Markov models. To evaluate the proposed method, experiments are performed both in simulation and on a real robot system.
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| 46. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
-
Learning grasp stability with tactile data and HMMs
- 2010
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Ingår i: IEEE International Symposium on Robot and Human Interactive Communication. - 1944-9437 .- 1944-9445. ; , s. 132-137
-
Konferensbidrag (refereegranskat)abstract
- In this paper, the problem of learning grasp stability in robotic object grasping based on tactile measurements is studied. Although grasp stability modeling and estimation has been studied for a long time, there are few robots today able of demonstrating extensive grasping skills. The main contribution of the work presented here is an investigation of probabilistic modeling for inferring grasp stability based on learning from examples. The main objective is classification of a grasp as stable or unstable before applying further actions on it, e.g. lifting. The problem cannot be solved by visual sensing which is typically used to execute an initial robot hand positioning with respect to the object. The output of the classification system can trigger a regrasping step if an unstable grasp is identified. An off-line learning process is implemented and used for reasoning about grasp stability for a three-fingered robotic hand using Hidden Markov models. To evaluate the proposed method, experiments are performed both in simulation and on a real robot system.
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| 47. |
- Bekiroglu, Yasemin, 1982-, et al.
(författare)
-
Learning Tactile Characterizations Of Object- And Pose-specific Grasps
- 2011
-
Konferensbidrag (refereegranskat)abstract
- Our aim is to predict the stability of a grasp from the perceptions available to a robot before attempting to lift up and transport an object. The percepts we consider consist of the tactile imprints and the object-gripper configuration read before and until the robot’s manipulator is fully closed around an object. Our robot is equipped with multiple tactile sensing arrays and it is able to track the pose of an object during the application of a grasp. We present a kernel-logistic-regression model of pose- and touch-conditional grasp success probability which we train on grasp data collected by letting the robot experience the effect on tactile and visual signals of grasps suggested by a teacher, and letting the robot verify which grasps can be used to rigidly control the object. We consider models defined on several subspaces of our input data – e.g., using tactile perceptions or pose information only. Our experiment demonstrates that joint tactile and pose-based perceptions carry valuable grasp-related information, as models trained on both hand poses and tactile parameters perform better than the models trained exclusively on one perceptual input.
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| 48. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
-
Learning Task- and Touch-based Grasping
- 2012
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In order to equip robots with goal-directed grasping ability, the integration of high-level task information with low-level sensory data is needed. For example, if a robot is given a task, e.g., pour me a cup of coffee, it needs to 1) make decision on which object to use, 2) how the hand should be placed around the object, and 3) how much gripping force should be applied so that the subsequent manipulation is feasible and stable for the pouring action. Several sensory streams (visual, proprioceptive and haptic) are relevant for these three steps. The problem domain and hence the state space becomes highdimensional involving both continuous and discrete variables with complex relations. We study how these can be encoded in a suitable manner using probabilistic generative models so that robots can achieve stable and robust goal-directed grasps by exploiting feedback loops from multisensory data.
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| 49. |
- Bekiroglu, Yasemin
(författare)
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Learning to Assess Grasp Stability from Vision, Touch and Proprioception
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Grasping and manipulation of objects is an integral part of a robot’s physical interaction with the environment. In order to cope with real-world situations, sensor based grasping of objects and grasp stability estimation is an important skill. This thesis addresses the problem of predicting the stability of a grasp from the perceptions available to a robot once fingers close around the object before attempting to lift it. A regrasping step can be triggered if an unstable grasp is identified. The percepts considered consist of object features (visual), gripper configurations (proprioceptive) and tactile imprints (haptic) when fingers contact the object. This thesis studies tactile based stability estimation by applying machine learning methods such as Hidden Markov Models. An approach to integrate visual and tactile feedback is also introduced to further improve the predictions of grasp stability, using Kernel Logistic Regression models.Like humans, robots are expected to grasp and manipulate objects in a goal-oriented manner. In other words, objects should be grasped so to afford subsequent actions: if I am to hammer a nail, the hammer should be grasped so to afford hammering. Most of the work on grasping commonly addresses only the problem of finding a stable grasp without considering the task/action a robot is supposed to fulfill with an object. This thesis also studies grasp stability assessment in a task-oriented way based on a generative approach using probabilistic graphical models, Bayesian Networks. We integrate high-level task information introduced by a teacher in a supervised setting with low-level stability requirements acquired through a robot’s exploration. The graphical model is used to encode probabilistic relationships between tasks and sensory data (visual, tactile and proprioceptive). The generative modeling approach enables inference of appropriate grasping configurations, as well as prediction of grasp stability. Overall, results indicate that the idea of exploiting learning approaches for grasp stability assessment is applicable in realistic scenarios.
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| 50. |
- Bekiroglu, Yasemin, 1982, et al.
(författare)
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Probabilistic Consolidation of Grasp Experience
- 2016
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - : IEEE conference proceedings. - 1050-4729. ; , s. 193-200
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Konferensbidrag (refereegranskat)abstract
- We present a probabilistic model for joint representation of several sensory modalities and action parameters in a robotic grasping scenario. Our non-linear probabilistic latent variable model encodes relationships between grasp-related parameters, learns the importance of features, and expresses confidence in estimates. The model learns associations between stable and unstable grasps that it experiences during an exploration phase. We demonstrate the applicability of the model for estimating grasp stability, correcting grasps, identifying objects based on tactile imprints and predicting tactile imprints from object-relative gripper poses. We performed experiments on a real platform with both known and novel objects, i.e., objects the robot trained with, and previously unseen objects. Grasp correction had a 75% success rate on known objects, and 73% on new objects. We compared our model to a traditional regression model that succeeded in correcting grasps in only 38% of cases.
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