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：我们通过设计仿真器来探索灵巧操作迁移问题。该任务旨在将人类操作迁移至灵巧机器人手的仿真环境，其固有难度源于其复杂、高度约束且不连续的动力学特性，以及需要控制具有高自由度的灵巧手以精确复现人类操作。先前在高保真黑盒仿真器或放宽约束的改进仿真器中进行优化的方法，仅展现出有限的能力或受限于仿真保真度不足。为克服这些限制，我们引入了参数化准物理仿真器与物理课程学习。核心思想在于：1）通过参数化仿真器课程在仿真保真度与可优化性之间取得平衡；2）在课程中的每个仿真器内求解问题，这些仿真器具备从高任务可优化性到高保真度的递进特性。我们成功实现了灵巧手在高保真仿真环境中跟踪复杂多样的人类操作，将成功率较最佳基线提升了11%以上。项目网站详见 https://meowuu7.github.io/QuasiSim/。