A hierarchical reinforcement learning framework with a CVAE contact-location model lets a parallel gripper grasp otherwise unreachable objects on tall walls by combining pushing, pivoting, and grasping, with 90% real-world success across six unseen objects.
Manipulation Trajectory Optimization with Online Grasp Synthesis and Selection
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In robot manipulation, planning the motion of a robot manipulator to grasp an object is a fundamental problem. A manipulation planner needs to generate a trajectory of the manipulator arm to avoid obstacles in the environment and plan an end-effector pose for grasping. While trajectory planning and grasp planning are often tackled separately, how to efficiently integrate the two planning problems remains a challenge. In this work, we present a novel method for joint motion and grasp planning. Our method integrates manipulation trajectory optimization with online grasp synthesis and selection, where we apply online learning techniques to select goal configurations for grasping, and introduce a new grasp synthesis algorithm to generate grasps online. We evaluate our planning approach and demonstrate that our method generates robust and efficient motion plans for grasping in cluttered scenes. Our video can be found at https://www.youtube.com/watch?v=LIcACf8YkGU .
citation-role summary
other 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls
A hierarchical reinforcement learning framework with a CVAE contact-location model lets a parallel gripper grasp otherwise unreachable objects on tall walls by combining pushing, pivoting, and grasping, with 90% real-world success across six unseen objects.