In-Hand Manipulation via Motion Cones
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Abstract (arXiv)
In this paper, we present the mechanics and algorithms to compute the set of feasible motions of an object pushed in a plane. This set is known as the motion cone and was previously described for non-prehensile manipulation tasks in the horizontal plane. We generalize its geometric construction to a broader set of planar tasks, where external forces such as gravity influence the dynamics of pushing, and prehensile tasks, where there are complex interactions between the gripper, object, and pusher. We show that the motion cone is defined by a set of low-curvature surfaces and provide a polyhedral cone approximation to it. We verify its validity with 2000 pushing experiments recorded with motion tracking system. Motion cones abstract the algebra involved in simulating frictional pushing by providing bounds on the set of feasible motions and by characterizing which pushes will stick or slip. We demonstrate their use for the dynamic propagation step in a sampling-based planning algorithm for in-hand manipulation. The planner generates trajectories that involve sequences of continuous pushes with 5-1000x speed improvements to equivalent algorithms. Video Summary -- https://youtu.be/tVDO8QMuYhc
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2018.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 3.
9 papers.
- 2021A System for General In-Hand Object Re-Orientationcited by 155
- 2021Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learningcited by 49
- 2022Learning to Grasp the Ungraspable with Emergent Extrinsic Dexteritycited by 25
- 2021Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assemblycited by 6
- 2021A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulationcited by 2
- 2026Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learningcited by 2
- 2025Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxationscited by 1
- 2026Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Setscited by 0
- 2026AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstructioncited by 0
- 2026AURORA: Active Uncertainty-Driven Re-Orientation for In-Hand Reconstruction8 Sep 2026
- 2026Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning10 Mar 2026
- 2026Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets15 Jan 2026
- 2025Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations11 Mar 2025
- 2022Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity2 Nov 2022
- 2021A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation9 Nov 2021
- 2021Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning4 Nov 2021
- 2021A System for General In-Hand Object Re-Orientation4 Nov 2021
- 2021Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assembly9 Oct 2021