35 research outputs found
Dexterous Manipulation Graphs
We propose the Dexterous Manipulation Graph as a tool to address in-hand
manipulation and reposition an object inside a robot's end-effector. This graph
is used to plan a sequence of manipulation primitives so to bring the object to
the desired end pose. This sequence of primitives is translated into motions of
the robot to move the object held by the end-effector. We use a dual arm robot
with parallel grippers to test our method on a real system and show successful
planning and execution of in-hand manipulation
Rearrangement-Based Manipulation via Kinodynamic Planning and Dynamic Planning Horizons
Robot manipulation in cluttered environments often requires complex and
sequential rearrangement of multiple objects in order to achieve the desired
reconfiguration of the target objects. Due to the sophisticated physical
interactions involved in such scenarios, rearrangement-based manipulation is
still limited to a small range of tasks and is especially vulnerable to
physical uncertainties and perception noise. This paper presents a planning
framework that leverages the efficiency of sampling-based planning approaches,
and closes the manipulation loop by dynamically controlling the planning
horizon. Our approach interleaves planning and execution to progressively
approach the manipulation goal while correcting any errors or path deviations
along the process. Meanwhile, our framework allows the definition of
manipulation goals without requiring explicit goal configurations, enabling the
robot to flexibly interact with all objects to facilitate the manipulation of
the target ones. With extensive experiments both in simulation and on a real
robot, we evaluate our framework on three manipulation tasks in cluttered
environments: grasping, relocating, and sorting. In comparison with two
baseline approaches, we show that our framework can significantly improve
planning efficiency, robustness against physical uncertainties, and task
success rate under limited time budgets.Comment: Accepted for publication in the Proceedings of the 2022 IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS 2022
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning
Rearranging objects on a tabletop surface by means of nonprehensile
manipulation is a task which requires skillful interaction with the physical
world. Usually, this is achieved by precisely modeling physical properties of
the objects, robot, and the environment for explicit planning. In contrast, as
explicitly modeling the physical environment is not always feasible and
involves various uncertainties, we learn a nonprehensile rearrangement strategy
with deep reinforcement learning based on only visual feedback. For this, we
model the task with rewards and train a deep Q-network. Our potential
field-based heuristic exploration strategy reduces the amount of collisions
which lead to suboptimal outcomes and we actively balance the training set to
avoid bias towards poor examples. Our training process leads to quicker
learning and better performance on the task as compared to uniform exploration
and standard experience replay. We demonstrate empirical evidence from
simulation that our method leads to a success rate of 85%, show that our system
can cope with sudden changes of the environment, and compare our performance
with human level performance.Comment: 2018 International Conference on Robotics and Automatio
Dexterous grasping under shape uncertainty
An important challenge in robotics is to achieve robust performance in object grasping and manipulation, dealing with noise and uncertainty. This paper presents an approach for addressing the performance of dexterous grasping under shape uncertainty. In our approach, the uncertainty in object shape is parameterized and incorporated as a constraint into grasp planning. The proposed approach is used to plan feasible hand con gurations for realizing planned contacts using different robotic hands. A compliant nger closing scheme is devised by exploiting both the object shape uncertainty and tactile sensing at ngertips. Experimental evaluation demonstrates that our method improves the performance of dexterous grasping under shape uncertainty