用快速采样实现足式机器人实时全身控制，效果出人意料。
Real-Time Whole-Body Control of Legged Robots with Model-Predictive Path Integral Control
- 基于MuJoCo并行采样，实现高效状态与动作轨迹搜索。
- 在真实机器人上完成平坦/不平地形行走、越障、推箱等任务。
- 首次在真实足式机器人上成功部署全身采样型模型预测控制。
本文提出一种系统，实现在真实世界足式机器人上的实时全身运动与操作策略生成。受近期机器人仿真进展启发，我们利用MuJoCo模拟器的高效并行能力，在多核CPU上快速采样机器人状态与动作轨迹。结果表明，采用极简控制策略即可实现令人惊讶的现实世界运动与操作能力。我们在多个硬件与仿真实验中验证了该方法：包括在平坦与不平地形上的稳健行走、越过高度与机器人相当的箱子，以及将箱子推至目标位置。据我们所知，这是首个在真实足式机器人硬件上成功部署全身采样型模型预测控制（MPC）的案例。实验视频与代码见：https://whole-body-mppi.github.io/
原文摘要 · Abstract (English)
This paper presents a system for enabling real-time synthesis of whole-body locomotion and manipulation policies for real-world legged robots. Motivated by recent advancements in robot simulation, we leverage the efficient parallelization capabilities of the MuJoCo simulator to achieve fast sampling over the robot state and action trajectories. Our results show surprisingly effective real-world locomotion and manipulation capabilities with a very simple control strategy. We demonstrate our approach on several hardware and simulation experiments: robust locomotion over flat and uneven terrains, climbing over a box whose height is comparable to the robot, and pushing a box to a goal position. To our knowledge, this is the first successful deployment of whole-body sampling-based MPC on real-world legged robot hardware. Experiment videos and code can be found at: https://whole-body-mppi.github.io/
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