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
this https URL

我们通过设计模拟器来探讨灵巧操作转移问题，使用参数化准物理模拟器和物理课程克服了之前的限制，成功使灵巧手在高保真度的模拟环境中追踪复杂多样的操作，成功率比最佳基准线提高了超过 11%。

QuasiSim：用于灵巧操作传递的参数化准物理模拟器

QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous  Manipulations Transfer

Many swarm robotics tasks consist of multiple conflicting objectives. This
research proposes a multi-objective evolutionary neural network approach to
developing controllers for swarms of robots. The swarm robot controllers are
trained in a low-fidelity Python simulator and then tested in a high-fidelity
simulated environment using Webots. Simulations are then conducted to test the
scalability of the evolved multi-objective robot controllers to environments
with a larger number of robots. The results presented demonstrate that the
proposed approach can effectively control each of the robots. The robot swarm
exhibits different behaviours as the weighting for each objective is adjusted.
The results also confirm that multi-objective neural network controllers
evolved in a low-fidelity simulator can be transferred to high-fidelity
simulated environments and that the controllers can scale to environments with
a larger number of robots without further retraining needed.

该研究提出了一种多目标进化神经网络方法，用于开发群体机器人的控制器。该方法通过低保真度的 Python 模拟器训练群体机器人控制器，然后在高保真度的 Webots 模拟环境中进行测试。结果表明，该方法可以有效控制每个机器人，且控制器具有可扩展性，而且不需要进一步重新训练。