QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer

• URL: http://arxiv.org/abs/2404.07988v2
• Date: Sun, 21 Jul 2024 06:56:12 GMT
• Title: QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer
• Authors: Xueyi Liu, Kangbo Lyu, Jieqiong Zhang, Tao Du, Li Yi,
• Abstract summary: We explore the dexterous manipulation transfer problem by designing simulators. The task wishes to transfer human manipulations to dexterous robot hand simulations. We successfully enable a dexterous hand to track complex and diverse manipulations in high-fidelity simulated environments.
• Score: 21.20658409070528
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We explore the dexterous manipulation transfer problem by designing simulators. The task wishes to transfer human manipulations to dexterous robot hand simulations and is inherently difficult due to its intricate, highly-constrained, and discontinuous dynamics and the need to control a dexterous hand with a DoF to accurately replicate human manipulations. Previous approaches that optimize in high-fidelity black-box simulators or a modified one with relaxed constraints only demonstrate limited capabilities or are restricted by insufficient simulation fidelity. We introduce parameterized quasi-physical simulators and a physics curriculum to overcome these limitations. The key ideas are 1) balancing between fidelity and optimizability of the simulation via a curriculum of parameterized simulators, and 2) solving the problem in each of the simulators from the curriculum, with properties ranging from high task optimizability to high fidelity. We successfully enable a dexterous hand to track complex and diverse manipulations in high-fidelity simulated environments, boosting the success rate by 11\%+ from the best-performed baseline. The project website is available at https://meowuu7.github.io/QuasiSim/.

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