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This paper presents Contact Mode Guided Manipulation Planning (CMGMP) for general 3D quasistatic and quasidynamic rigid body motion planning in dexterous manipulation. The CMGMP algorithm generates hybrid motion plans including both continuous state transitions and discrete contact mode switches, without the need for pre-specified contact sequences or pre-designed motion primitives. The key idea is to use automatically enumerated contact modes to guide the tree expansions during the search. Contact modes automatically synthesize manipulation primitives, while the sampling-based planning framework sequences those primitives into a coherent plan. We test our algorithm on many simulated 3D manipulation tasks, and validate our models by executing the plans open-loop on a real robot-manipulator system.
The discontinuities and multi-modality introduced by contacts make manipulation planning challenging. Many previous works avoid this problem by pre-designing a set of high-level motion primitives like grasping and pushing. However, such motion primit
Dexterous manipulation has been a long-standing challenge in robotics. Recently, modern model-free RL has demonstrated impressive results on a number of problems. However, complex domains like dexterous manipulation remain a challenge for RL due to t
Sampling-based motion planners rely on incremental densification to discover progressively shorter paths. After computing feasible path $xi$ between start $x_s$ and goal $x_t$, the Informed Set (IS) prunes the configuration space $mathcal{C}$ by cons
This report describes our approach for Phase 3 of the Real Robot Challenge. To solve cuboid manipulation tasks of varying difficulty, we decompose each task into the following primitives: moving the fingers to the cuboid to grasp it, turning it on th
Learning dexterous manipulation in high-dimensional state-action spaces is an important open challenge with exploration presenting a major bottleneck. Although in many cases the learning process could be guided by demonstrations or other suboptimal e