发表机构
Light Origins（Light Origins）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出 Light-Loco-Parkour 系统，通过多技能蒸馏构建端到端感知全身运动策略，实现人形机器人在复杂地形的自主运动，且可零样本迁移至真实硬件。
AI 中文摘要
现有人形机器人全身控制系统仍未达到人类在杂乱地形中的运动方式：它们要么跟踪无地形泛化的表达性全身参考，要么在线对地形做出反应，却基本不使用手臂、躯干和膝盖。我们提出 Light-Loco-Parkour（LLP），这是一种端到端的感知型全身运动系统，通过单个可部署策略填补了这一空白。该策略仅以机载深度数据和速度指令为条件，决定何时行走、平衡、攀爬、下台阶或 vault（ vault 为专业术语，保留原名），无需参考输入、技能标签、手动编码的门控或运行时运动图。与现有人形机器人系统相比，LLP 有三项贡献：第一，它引入了一种全身感知控制流水线，该流水线将经强化学习（RL）训练的速度跟踪运动策略与从物体交互运动中学习的跑酷（parkour）技能相结合，使同一策略能在开阔地形跟踪速度、在障碍物处执行全身穿越并在之后恢复运动；第二，它通过将单个运动扩展为跨障碍物几何结构的动态可行、地形配对参考，从稀疏种子中获取地形条件技能，而非依赖大量运动语料库；第三，它从奖励中学习自主技能转换，让策略仅从深度数据和指令中决定何时调用以及调用哪种全身技能，无需独热技能标签、手动编码状态机或运行时运动生成器。仿真和真实世界实验显示，其在基准地形和未见障碍物变体上均有高成功率，且同一策略可零样本迁移至室内和室外硬件实验。这些结果证明，人形机器人仅使用机载感知和单个可部署策略即可在室外环境中实现自主感知型全身运动。
英文摘要
Existing humanoid whole-body control systems still fall short of the way humans move through cluttered terrain: they either track expressive whole-body references without terrain generalization, or react to terrain online while leaving the arms, torso, and knees largely unused. We present \texttt{Light-Loco-Parkour} (LLP), an end-to-end perceptive whole-body locomotion system that closes this gap with a single deployable policy. Conditioned only on onboard depth and a velocity command, the policy decides when to walk, balance, climb, step down, or vault, with no reference input, skill label, hand-coded gate, or runtime motion graph. Compared with prior humanoid systems, LLP makes three contributions. First, it introduces a whole-body perceptive-control pipeline that extends an RL-trained, velocity-tracking locomotion policy with parkour skills learned from object-interacting motions, so the same policy tracks velocity in open terrain, executes whole-body traversal at obstacles, and resumes locomotion afterward. Second, it acquires terrain-conditioned skills from sparse seeds by expanding a single motion into dynamically feasible, terrain-paired references across obstacle geometry, rather than relying on a large motion corpus. Third, it learns autonomous skill transitions from reward, letting the policy decide when and which whole-body skill to invoke from depth and command alone, with no one-hot skill label, hand-coded state machine, or runtime motion generator. Simulation and real-world experiments show high success across both benchmarked terrains and unseen obstacle variations, and the same policy transfers zero-shot to indoor and outdoor hardware experiments. These results demonstrate autonomous perceptive whole-body locomotion on a humanoid in outdoor settings, using only onboard sensing and a single deployable policy.
Commentshttps://light-loco-parkour.github.io/
