Lober Ryan, Sigaud Olivier, Padois Vincent
Fuzzy Logic Robotics, Paris, France.
Institut des Systèmes Intelligents et de Robotique, Sorbonne Université, CNRS UMR 7222, Paris, France.
Front Robot AI. 2020 Jun 4;7:61. doi: 10.3389/frobt.2020.00061. eCollection 2020.
Producing feasible motions for highly redundant robots, such as humanoids, is a complicated and high-dimensional problem. Model-based whole-body control of such robots can generate complex dynamic behaviors through the simultaneous execution of multiple tasks. Unfortunately, tasks are generally planned without close consideration for the underlying controller being used, or the other tasks being executed, and are often infeasible when executed on the robot. Consequently, there is no guarantee that the motion will be accomplished. In this work, we develop a proof-of-concept optimization loop which automatically improves task feasibility using model-free policy search in conjunction with model-based whole-body control. This combination allows problems to be solved, which would be otherwise intractable using simply one or the other. Through experiments on both the simulated and real iCub humanoid robot, we show that by optimizing task feasibility, initially infeasible complex dynamic motions can be realized-specifically, a sit-to-stand transition. These experiments can be viewed in the accompanying Video S1.
为类人机器人等高度冗余的机器人生成可行的动作是一个复杂的高维问题。基于模型的此类机器人全身控制可以通过同时执行多个任务来生成复杂的动态行为。不幸的是，任务规划通常没有充分考虑所使用的底层控制器或正在执行的其他任务，并且在机器人上执行时往往不可行。因此，无法保证动作能够完成。在这项工作中，我们开发了一个概念验证优化循环，该循环结合基于模型的全身控制，使用无模型策略搜索自动提高任务可行性。这种结合使得一些问题得以解决，而仅使用其中一种方法则难以处理这些问题。通过在模拟和真实的iCub类人机器人上进行的实验，我们表明，通过优化任务可行性，可以实现最初不可行的复杂动态动作——具体来说，就是从坐姿到站姿的转换。这些实验可以在随附的视频S1中观看。
