论文标题
通过局部平滑进行准动力接触模型的全球计划，以进行接触丰富的操纵
Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models
论文作者
论文摘要
从基于模型的角度来看，强化学习（RL）在富含接触的操作中的经验成功尚待理解，在基于模型的角度来看，关键困难通常归因于（i）接触模式的爆炸，（ii）僵硬，非平滑的触点动态及其由此产生的爆炸 /不连续的梯度和（IIII）的爆炸性启动，并构成了非局部性问题。 RL的随机性质通过有效采样和平均接触模式来解决（i）和（ii）。另一方面，基于模型的方法通过分析平滑接触动态来应对相同的挑战。我们的第一个贡献是为简单系统建立两种方法的理论等效性，并在许多复杂示例上提供定性和经验的等效性。为了进一步减轻（II），我们的第二个贡献是凸面的凸面，可区分和准动力的触点动力学表述，这对两种平滑方案都可以调整，并且通过实验证明了对接触富含接触的计划非常有效。我们的最终贡献解决了（III），在其中我们表明，当通过平滑度抽象接触模式时，基于经典抽样的运动计划算法在全球计划中可以有效。将我们的方法应用于具有挑战性的接触式操纵任务的集合中，我们证明了基于模型的有效运动计划可以实现与RL相当的结果，并且计算较少。视频：https：//youtu.be/12ew4xc-vwa
The empirical success of Reinforcement Learning (RL) in the setting of contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficulties are often attributed to (i) the explosion of contact modes, (ii) stiff, non-smooth contact dynamics and the resulting exploding / discontinuous gradients, and (iii) the non-convexity of the planning problem. The stochastic nature of RL addresses (i) and (ii) by effectively sampling and averaging the contact modes. On the other hand, model-based methods have tackled the same challenges by smoothing contact dynamics analytically. Our first contribution is to establish the theoretical equivalence of the two methods for simple systems, and provide qualitative and empirical equivalence on a number of complex examples. In order to further alleviate (ii), our second contribution is a convex, differentiable and quasi-dynamic formulation of contact dynamics, which is amenable to both smoothing schemes, and has proven through experiments to be highly effective for contact-rich planning. Our final contribution resolves (iii), where we show that classical sampling-based motion planning algorithms can be effective in global planning when contact modes are abstracted via smoothing. Applying our method on a collection of challenging contact-rich manipulation tasks, we demonstrate that efficient model-based motion planning can achieve results comparable to RL with dramatically less computation. Video: https://youtu.be/12Ew4xC-VwA
