Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (RL), one popular approach has been to use massively-parallelized, domain-randomized simulations to learn a policy offline over a vast array of contact conditions, allowing robust sim-to-real transfer. Inspired by recent advances in real-time parallel simulation, this work considers instead the viability of online planning methods for contact-rich manipulation by studying the well-known in-hand cube reorientation task. We propose a simple architecture that employs a sampling-based predictive controller and vision-based pose estimator to search for contact-rich control actions online. We conduct thorough experiments to assess the real-world performance of our method, architectural design choices, and key factors for robustness, demonstrating that our simple sampling-based approach achieves performance comparable to prior RL-based works. Supplemental material: https://caltech-amber.github.io/drop.

翻译：实现类人灵巧性是机器人学中长期存在的挑战，部分原因在于接触密集型系统的规划与控制复杂性。在强化学习领域，一种主流方法是通过大规模并行化、领域随机化的仿真，在大量接触条件下离线学习策略，从而实现稳健的仿真到现实迁移。受实时并行仿真最新进展的启发，本研究通过经典的手内立方体重定向任务，探讨在线规划方法在接触密集型操作中的可行性。我们提出一种简洁的架构，该架构采用基于采样的预测控制器与基于视觉的位姿估计器，在线搜索接触密集型控制动作。我们通过详尽的实验评估了所提方法的实际性能、架构设计选择及鲁棒性关键因素，证明这种基于采样的简洁方法能达到与先前基于强化学习的研究相当的性能。补充材料：https://caltech-amber.github.io/drop。