一个可复用的通用四足机器人接触控制策略，支持多种运动与操作任务。
GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots
- 用深度强化学习训练通用接触控制器，可跟踪任意接触点。
- 单个策略在复杂地形和物体交互任务中表现稳定，无需重新训练。
- 适合需要快速适配新任务的四足机器人研发人员。
当前多数四足机器人运动控制方法依赖深度强化学习从零开始端到端训练策略，但每项新任务都需耗时调整奖励函数。本文提出通用接触条件策略（GeCCo），一种基于深度强化学习训练的底层控制策略，可让四足机器人跟踪任意接触点。该方法优势在于提供通用、模块化的低层控制器，适用于更广泛的高层任务，无需从头训练新控制器。我们在统一框架下评估了多种运动与操作任务，包括多种步态、复杂地形（如台阶、斜坡）、未见的踏板与窄梁，以及物体交互（如按按钮、轨迹追踪）。通过结合特定任务的高层接触规划器与预训练的通用策略，新行为可高效获得。补充视频见 https://youtu.be/o8Dd44MkG2E。
原文摘要 · Abstract (English)
Most modern approaches to quadruped locomotion focus on using Deep Reinforcement Learning (DRL) to learn policies from scratch, in an end-to-end manner. Such methods often fail to scale, as every new problem or application requires time-consuming and iterative reward definition and tuning. We present Generalist Contact-Conditioned Policy (GeCCo) -- a low-level policy trained with Deep Reinforcement Learning that is capable of tracking arbitrary contact points on a quadruped robot. The strength of our approach is that it provides a general and modular low-level controller that can be reused for a wider range of high-level tasks, without the need to re-train new controllers from scratch. We demonstrate the scalability and robustness of our method by evaluating on a wide range of locomotion and manipulation tasks in a common framework and under a single generalist policy. These include a variety of gaits, traversing complex terrains (eg. stairs and slopes) as well as previously unseen stepping-stones and narrow beams, and interacting with objects (eg. pushing buttons, tracking trajectories). Our framework acquires new behaviors more efficiently, simply by combining a task-specific high-level contact planner and the pre-trained generalist policy. A supplementary video can be found at https://youtu.be/o8Dd44MkG2E.
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