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/.

翻译：我们通过设计模拟器来探索灵巧操作迁移问题。该任务旨在将人类操作迁移到灵巧机器人手模拟中，由于其动力学特性复杂、强约束、不连续，且需要控制具有多个自由度的灵巧手精确复现人类操作，因此本质上是困难的。先前方法或在保真度高的黑盒模拟器中进行优化，或使用放宽约束的改进模拟器，但仅表现出有限的能力，或受限于模拟保真度不足。我们引入了参数化准物理模拟器及物理课程来克服这些局限。核心思路是：1) 通过参数化模拟器课程在模拟的保真度与可优化性之间取得平衡；2) 在课程中每个从高任务可优化性到高保真度不同特性的模拟器中求解问题。我们成功使灵巧手在高保真度模拟环境中追踪复杂多样的操作，相比最优基线方法，成功率提升超过11%。项目网站见 https://meowuu7.github.io/QuasiSim/。