| 1. |
- Haustein, Joshua Alexander, 1987-, et al.
(författare)
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Learning Manipulation States and Actions for Efficient Non-prehensile Rearrangement Planning
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- This paper addresses non-prehensile rearrangement planning problems where a robot is tasked to rearrange objects among obstacles on a planar surface. We present an efficient planning algorithm that is designed to impose few assumptions on the robot's non-prehensile manipulation abilities and is simple to adapt to different robot embodiments. For this, we combine sampling-based motion planning with reinforcement learning and generative modeling. Our algorithm explores the composite configuration space of objects and robot as a search over robot actions, forward simulated in a physics model. This search is guided by a generative model that provides robot states from which an object can be transported towards a desired state, and a learned policy that provides corresponding robot actions. As an efficient generative model, we apply Generative Adversarial Networks. We implement and evaluate our approach for robots endowed with configuration spaces in SE(2). We demonstrate empirically the efficacy of our algorithm design choices and observe more than 2x speedup in planning time on various test scenarios compared to a state-of-the-art approach.
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| 2. |
- Haustein, Joshua Alexander, 1987-, et al.
(författare)
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Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions
- 2018
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Ingår i: Workshop on "Machine Learning in Robot Motion Planning" at the International Conference on Intelligent Robots and Systems (IROS) 2018.
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Konferensbidrag (refereegranskat)abstract
- n this work we combine sampling-based motionplanning with reinforcement learning and generative modelingto solve non-prehensile rearrangement problems. Our algorithmexplores the composite configuration space of objects and robotas a search over robot actions, forward simulated in a physicsmodel. This search is guided by a generative model thatprovides robot states from which an object can be transportedtowards a desired state, and a learned policy that providescorresponding robot actions. As an efficient generative model,we apply Generative Adversarial Networks.
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| 3. |
- Cruciani, Silvia, 1991-, et al.
(författare)
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Dual-Arm In-Hand Manipulation Using Visual Feedback
- 2019
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Ingår i: IEEE-RAS International Conference on Humanoid Robots. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538676301 ; , s. 387-394
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Konferensbidrag (refereegranskat)abstract
- In this work, we address the problem of executing in-hand manipulation based on visual input. Given an initial grasp, the robot has to change its grasp configuration without releasing the object. We propose a method for in-hand manipulation planning and execution based on information on the object's shape using a dual-Arm robot. From the available information on the object, which can be a complete point cloud but also partial data, our method plans a sequence of rotations and translations to reconfigure the object's pose. This sequence is executed using non-prehensile pushes defined as relative motions between the two robot arms.
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| 4. |
- Hang, Kaiyu, 1987-, et al.
(författare)
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A Framework for Optimal Grasp Contact Planning
- 2017
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2377-3766. ; 2:2, s. 704-711
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Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of finding grasp contacts that are optimal under a given grasp quality function on arbitrary objects. Our approach formulates a framework for contact-level grasping as a path finding problem in the space of supercontact grasps. The initial supercontact grasp contains all grasps and in each step along a path grasps are removed. For this, we introduce and formally characterize search space structure and cost functions underwhich minimal cost paths correspond to optimal grasps. Our formulation avoids expensive exhaustive search and reduces computational cost by several orders of magnitude. We present admissible heuristic functions and exploit approximate heuristic search to further reduce the computational cost while maintaining bounded suboptimality for resulting grasps. We exemplify our formulation with point-contact grasping for which we define domain specific heuristics and demonstrate optimality and bounded suboptimality by comparing against exhaustive and uniform cost search on example objects. Furthermore, we explain how to restrict the search graph to satisfy grasp constraints for modeling hand kinematics. We also analyze our algorithm empirically in terms of created and visited search states and resultant effective branching factor.
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| 5. |
- Hang, Kaiyu, 1987-
(författare)
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Dexterous Grasping : Representation and Optimization
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Many robot object interactions require that an object is firmly held, and that the grasp remains stable during the whole manipulation process. Based on grasp wrench space, this thesis address the problems of measuring the grasp sensitivity against friction changes, planning contacts and hand configurations on mesh and point cloud representations of arbitrary objects, planning adaptable grasps and finger gaiting for keeping a grasp stable under various external disturbances, as well as learning of grasping manifolds for more accurate reachability and inverse kinematics computation for multifingered grasping. Firstly, we propose a new concept called friction sensitivity, which measures how susceptible a specific grasp is to changes in the underlying frictionc oefficients. We develop algorithms for the synthesis of stable grasps with low friction sensitivity and for the synthesis of stable grasps in the case of small friction coefficients. Secondly, for fast planning of contacts and hand configurations for dexterous grasping, as well as keeping the stability of a grasp during execution, we present a unified framework for grasp planning and in-hand grasp adaptation using visual, tactile and proprioceptive feedback. The main objective of the proposed framework is to enable fingertip grasping by addressing problems of changed weight of the object, slippage and external disturbances. For this purpose, we introduce the Hierarchical Fingertip Space (HFTS) as a representation enabling optimization for both efficient grasp synthesis and online finger gaiting. Grasp synthesis is followed by a grasp adaptation step that consists of both grasp force adaptation through impedance control and regrasping/finger gaiting when the former is not sufficient. Lastly, to improve the efficiency and accuracy of dexterous grasping and in-hand manipulation, we present a system for fingertip grasp planning that incrementally learns a heuristic for hand reachability and multi-fingered inverse kinematics. During execution the system plans and executes fingertip grasps using Canny’s grasp quality metric and a learned random forest based hand reachability heuristic. In the offline module, this heuristic is improved based on a grasping manifold that is incrementally learned from the experiences collected during execution.
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| 6. |
- Hang, Kaiyu, 1987-, et al.
(författare)
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Hierarchical Fingertip Space : A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation
- 2016
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Ingår i: IEEE Transactions on robotics. - : IEEE Press. - 1552-3098 .- 1941-0468. ; 32:4, s. 960-972
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Tidskriftsartikel (refereegranskat)abstract
- We present a unified framework for grasp planning and in-hand grasp adaptation using visual, tactile and proprioceptive feedback. The main objective of the proposed framework is to enable fingertip grasping by addressing problems of changed weight of the object, slippage and external disturbances. For this purpose, we introduce the Hierarchical Fingertip Space (HFTS) as a representation enabling optimization for both efficient grasp synthesis and online finger gaiting. Grasp synthesis is followed by a grasp adaptation step that consists of both grasp force adaptation through impedance control and regrasping/finger gaiting when the former is not sufficient. Experimental evaluation is conducted on an Allegro hand mounted on a Kuka LWR arm.
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| 7. |
- Hang, Kaiyu, 1987-, et al.
(författare)
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Team CVAP’s Mobile Picking System at the Amazon Picking Challenge 2015
- 2020
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Ingår i: Advances on Robotic Item Picking. - Cham : Springer Nature. ; , s. 1-12
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we present the system we developed for the Amazon Picking Challenge 2015, and discuss some of the lessons learned that may prove useful to researchers and future teams developing autonomous robot picking systems. For the competition we used a PR2 robot, which is a dual arm robot research platform equipped with a mobile base and a variety of 2D and 3D sensors. We adopted a behavior tree to model the overall task execution, where we coordinate the different perception, localization, navigation, and manipulation activities of the system in a modular fashion. Our perception system detects and localizes the target objects in the shelf and it consisted of two components: one for detecting textured rigid objects using the SimTrack vision system, and one for detecting non-textured or nonrigid objects using RGBD features. In addition, we designed a set of grasping strategies to enable the robot to reach and grasp objects inside the confined volume of shelf bins. The competition was a unique opportunity to integrate the work of various researchers at the Robotics, Perception and Learning laboratory (formerly the Computer Vision and Active Perception Laboratory, CVAP) of KTH, and it tested the performance of our robotic system and defined the future direction of our research.
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| 8. |
- Haustein, Joshua A., et al.
(författare)
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Placing Objects with prior In-Hand Manipulation using Dexterous Manipulation Graphs
- 2019
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Konferensbidrag (refereegranskat)abstract
- We address the problem of planning the placement of a grasped object with a robot manipulator. More specifically, the robot is tasked to place the grasped object such that a placement preference function is maximized. For this, we present an approach that uses in-hand manipulation to adjust the robot’s initial grasp to extend the set of reachable placements. Given an initial grasp, the algorithm computes a set of grasps that can be reached by pushing and rotating the object in-hand. With this set of reachable grasps, it then searches for a stable placement that maximizes the preference function. If successful it returns a sequence of in-hand pushes to adjust the initial grasp to a more advantageous grasp together with a transport motion that carries the object to the placement. We evaluate our algorithm’s performance on various placing scenarios, and observe its effectiveness also in challenging scenes containing many obstacles. Our experiments demonstrate that re-grasping with in-hand manipulation increases the quality of placements the robot can reach. In particular, it enables the algorithm to find solutions in situations where safe placing with the initial grasp wouldn’t be possible.
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| 9. |
- Haustein, Joshua, et al.
(författare)
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Object Placement Planning and optimization for Robot Manipulators
- 2019
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Ingår i: IEEE International Conference on Intelligent Robots and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728140049 - 9781728140056 ; , s. 7417-7424
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Konferensbidrag (refereegranskat)abstract
- We address the problem of planning the placement of a rigid object with a dual-arm robot in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot and c) optimizes a user-given placement objective. In addition, we need to select which robot arm to perform the placement with. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning with a novel hierarchical search for suitable placement poses. Our algorithm incrementally produces approach motions to stable placement poses, reaching placements with better objective as runtime progresses. We evaluate our approach for two different placement objectives, and observe its effectiveness even in challenging scenarios.
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| 10. |
- Song, Haoran, et al.
(författare)
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Multi-Object Rearrangement with Monte Carlo Tree Search : A Case Study on Planar Nonprehensile Sorting
- 2020
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Konferensbidrag (refereegranskat)abstract
- In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated and real-world sorting tasks. We observe that the algorithm is capable of reliably sorting large number of convex and non-convex objects, as well as convex objects in the presence of immovable obstacles.
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