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Dynamic On-Palm Manipulation via Controlled Sliding

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arxiv 2405.08731 v2 pith:ENGA22CV submitted 2024-05-14 cs.RO

classification cs.RO
keywords manipulationdynamictaskcontactcontact-implicitnon-prehensileslidingconsideration
verification ladder T0 review T1 audit T2 compute T3 formal
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Non-prehensile manipulation enables fast interactions with objects by circumventing the need to grasp and ungrasp as well as handling objects that cannot be grasped through force closure. Current approaches to non-prehensile manipulation focus on static contacts, avoiding the underactuation that comes with sliding. However, the ability to control sliding contact, essentially removing the no-slip constraint, opens up new possibilities in dynamic manipulation. In this paper, we explore a challenging dynamic non-prehensile manipulation task that requires the consideration of the full spectrum of hybrid contact modes. We leverage recent methods in contact-implicit MPC to handle the multi-modal planning aspect of the task. We demonstrate, with careful consideration of integration between the simple model used for MPC and the low-level tracking controller, how contact-implicit MPC can be adapted to dynamic tasks. Surprisingly, despite the known inaccuracies of frictional rigid contact models, our method is able to react to these inaccuracies while still quickly performing the task. Moreover, we do not use common aids such as reference trajectories or motion primitives, highlighting the generality of our approach. To the best of our knowledge, this is the first application of contact-implicit MPC to a dynamic manipulation task in three dimensions.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Global Contact-Rich Planning with Sparsity-Rich Semidefinite Relaxations

    cs.RO 2025-02 conditional novelty 7.0 of 10

    Sparse semidefinite relaxations, exploiting correlative, term, and robotics-specific sparsity, solve contact-rich planning problems to certified near-global optimality in seconds for several benchmark tasks.

  2. On the Surprising Robustness of Sequential Convex Optimization for Contact-Implicit Motion Planning

    math.OC 2025-02 conditional novelty 6.0 of 10

    CRISP is a primal-only sequential convex programming solver with a weighted l1 penalty merit function that solves contact-implicit motion planning problems from all-zero initialization.

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