Generative Attention Learning (GenerAL) is a framework for high-DOF multi-fingered grasping that is not only robust to
dense clutter and novel objects but also effective with a variety of different parallel-jaw and multi-fingered robot hands. This
framework introduces a novel attention mechanism that substantially improves the grasp success rate in clutter. Its generative
nature allows the learning of full-DOF grasps with flexible end-effector positions and orientations, as well as all finger joint
angles of the hand. Trained purely in simulation, this framework skillfully closes the sim-to-real gap. To close the visual
sim-to-real gap, this framework uses a single depth image as input. To close the dynamics sim-to-real gap, this framework
circumvents continuous motor control with a direct mapping from pixel to Cartesian space inferred from the same depth
image. Finally, this framework demonstrates inter-robot generality by achieving over 92% real-world grasp success rates in
cluttered scenes with novel objects using two multi-fingered robotic hand-arm systems with different degrees of freedom.
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This content will become publicly available on December 4, 2024
Effective and Robust Non-Prehensile Manipulation via Persistent Homology Guided Monte-Carlo Tree Search
Performing object retrieval in real-world workspaces must tackle challenges including uncertainty and clutter. One option is to apply prehensile operations, which can be time consuming in highly-cluttered scenarios. On the other hand, non-prehensile actions, such as pushing simultaneously multiple objects, can help to quickly clear a cluttered workspace and retrieve a target object. Such actions, however, can also lead to increased uncertainty as it is difficult to estimate the outcome of pushing operations. The proposed framework in this work integrates topological tools and Monte-Carlo Tree Search (MCTS) to achieve effective and robust pushing for object retrieval. It employs persistent homology to automatically identify manageable clusters of blocking objects without the need for manually adjusting hyper-parameters. Then, MCTS uses this information to explore feasible actions to push groups of objects, aiming to minimize the number of operations needed to clear the path to the target. Real-world experiments using a Baxter robot, which involves some noise in actuation, show that the proposed framework achieves a higher success rate in solving retrieval tasks in dense clutter than alternatives. Moreover, it produces solutions with few pushing actions improving the overall execution time. More critically, it is robust enough that it allows one to plan the sequence of actions offline and then execute them reliably on a Baxter robot.
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- NSF-PAR ID:
- 10540542
- Publisher / Repository:
- International Symposium on Experimental Robotics (ISER)
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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