让机器人在不平地形上完成翻越、滚翻等高动态动作
Deep Whole-body Parkour
- 将环境感知融入全身运动追踪，统一控制范式
- 单个策略实现多种复杂动作，在非结构化地形上稳定执行
- 适合研究复杂人形机器人控制与动态任务规划的学者
当前人形机器人控制主要分为两类：感知型步态控制能适应复杂地形但仅限于步行；通用运动追踪可复现复杂技能却忽略环境约束。本文提出一种新框架，将外感受感知整合至全身运动追踪中，使机器人能在不平整地形上执行高度动态的非步态任务。通过训练单一策略在多样地形上完成多种不同动作，验证了感知融入控制回路的显著优势。实验表明，该框架可实现稳健的高动态多接触运动，如翻越、俯身滚翻等，大幅扩展了机器人在非结构化地形上的通行能力，远超简单行走或奔跑。
原文摘要 · Abstract (English)
Current approaches to humanoid control generally fall into two paradigms: perceptive locomotion, which handles terrain well but is limited to pedal gaits, and general motion tracking, which reproduces complex skills but ignores environmental capabilities. This work unites these paradigms to achieve perceptive general motion control. We present a framework where exteroceptive sensing is integrated into whole-body motion tracking, permitting a humanoid to perform highly dynamic, non-locomotion tasks on uneven terrain. By training a single policy to perform multiple distinct motions across varied terrestrial features, we demonstrate the non-trivial benefit of integrating perception into the control loop. Our results show that this framework enables robust, highly dynamic multi-contact motions, such as vaulting and dive-rolling, on unstructured terrain, significantly expanding the robot's traversability beyond simple walking or running. https://project-instinct.github.io/deep-whole-body-parkour
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