| 1. |
- Mitsioni, Ioanna, 1991-, et al.
(författare)
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Interpretability in Contact-Rich Manipulation via Kinodynamic Images
- 2021
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Ingår i: Proceedings - IEEE International Conference on Robotics and Automation. - : Institute of Electrical and Electronics Engineers (IEEE). - 1050-4729. ; 2021-May, s. 10175-10181
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Konferensbidrag (refereegranskat)abstract
- Deep Neural Networks (NNs) have been widely utilized in contact-rich manipulation tasks to model the complicated contact dynamics. However, NN-based models are often difficult to decipher which can lead to seemingly inexplicable behaviors and unidentifiable failure cases. In this work, we address the interpretability of NN-based models by introducing the kinodynamic images. We propose a methodology that creates images from kinematic and dynamic data of contact-rich manipulation tasks. By using images as the state representation, we enable the application of interpretability modules that were previously limited to vision-based tasks. We use this representation to train a Convolutional Neural Network (CNN) and we extract interpretations with Grad-CAM to produce visual explanations. Our method is versatile and can be applied to any classification problem in manipulation tasks to visually interpret which parts of the input drive the model's decisions and distinguish its failure modes, regardless of the features used. Our experiments demonstrate that our method enables detailed visual inspections of sequences in a task, and high-level evaluations of a model's behavior.
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| 2. |
- Kokic, Mia, et al.
(författare)
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Affordance detection for task-specific grasping using deep learning
- 2017
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Ingår i: 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids). - : IEEE conference proceedings. - 9781538646786 ; , s. 91-98
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Konferensbidrag (refereegranskat)abstract
- In this paper we utilize the notion of affordances to model relations between task, object and a grasp to address the problem of task-specific robotic grasping. We use convolutional neural networks for encoding and detecting object affordances, class and orientation, which we utilize to formulate grasp constraints. Our approach applies to previously unseen objects from a fixed set of classes and facilitates reasoning about which tasks an object affords and how to grasp it for that task. We evaluate affordance detection on full-view and partial-view synthetic data and compute task-specific grasps for objects that belong to ten different classes and afford five different tasks. We demonstrate the feasibility of our approach by employing an optimization-based grasp planner to compute task-specific grasps.
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| 3. |
- Björkman, Mårten, 1970-, et al.
(författare)
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Enhancing Visual Perception of Shape through Tactile Glances
- 2013
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Ingår i: Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on. - : IEEE conference proceedings. - 2153-0866 .- 2153-0858. - 9781467363587 ; , s. 3180-3186
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Konferensbidrag (refereegranskat)abstract
- Object shape information is an important parameter in robot grasping tasks. However, it may be difficult to obtain accurate models of novel objects due to incomplete and noisy sensory measurements. In addition, object shape may change due to frequent interaction with the object (cereal boxes, etc). In this paper, we present a probabilistic approach for learning object models based on visual and tactile perception through physical interaction with an object. Our robot explores unknown objects by touching them strategically at parts that are uncertain in terms of shape. The robot starts by using only visual features to form an initial hypothesis about the object shape, then gradually adds tactile measurements to refine the object model. Our experiments involve ten objects of varying shapes and sizes in a real setup. The results show that our method is capable of choosing a small number of touches to construct object models similar to real object shapes and to determine similarities among acquired models.
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| 4. |
- Caccamo, Sergio, et al.
(författare)
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Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
- 2016
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2153-0858. - 9781509037629 ; , s. 582-589
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Konferensbidrag (refereegranskat)abstract
- In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an online probabilistic framework that merges visual and tactile measurements using Gaussian Random Field and Gaussian Process Implicit Surfaces. The system investigates incomplete point clouds in order to find a small set of regions of interest which are then physically explored with a robotic arm equipped with tactile sensors. We show experimental results obtained using a PrimeSense camera, a Kinova Jaco2 robotic arm and Optoforce sensors on different scenarios. We then demostrate how to use the online framework for object detection and terrain classification.
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| 5. |
- Viña, Francisco, 1990-, et al.
(författare)
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Predicting Slippage and Learning Manipulation Affordances through Gaussian Process Regression
- 2013
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Ingår i: IEEE-RAS International Conference on Humanoid Robots. - : IEEE Computer Society. - 2164-0580 .- 2164-0572. ; , s. 462-468
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Konferensbidrag (refereegranskat)abstract
- Object grasping is commonly followed by some form of object manipulation - either when using the grasped object as a tool or actively changing its position in the hand through in-hand manipulation to afford further interaction. In this process, slippage may occur due to inappropriate contact forces, various types of noise and/or due to the unexpected interaction or collision with the environment. In this paper, we study the problem of identifying continuous bounds on the forces and torques that can be applied on a grasped object before slippage occurs. We model the problem as kinesthetic rather than cutaneous learning given that the measurements originate from a wrist mounted force-torque sensor. Given the continuous output, this regression problem is solved using a Gaussian Process approach. We demonstrate a dual armed humanoid robot that can autonomously learn force and torque bounds and use these to execute actions on objects such as sliding and pushing. We show that the model can be used not only for the detection of maximum allowable forces and torques but also for potentially identifying what types of tasks, denoted as manipulation affordances, a specific grasp configuration allows. The latter can then be used to either avoid specific motions or as a simple step of achieving in-hand manipulation of objects through interaction with the environment.
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| 6. |
- Gao, Yuan, et al.
(författare)
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Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human-Robot Interaction
- 2019
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Ingår i: Proceedings 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728140049 ; , s. 305-312
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Konferensbidrag (refereegranskat)abstract
- In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model.
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| 7. |
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| 8. |
- Antonova, Rika, et al.
(författare)
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Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
- 2018
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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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| 9. |
- 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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| 10. |
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