拥抱碰撞：用于可部署接触无关运动的人形机器人跟随
Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics Motions
AI总结：
针对人形机器人全身复杂接触控制的挑战，提出一种通用运动框架，利用GPU加速模拟器训练全身控制策略，使机器人能在随机接触和极端躯干旋转下实时执行运动指令，实现接触无关的部署。
摘要：
以往的人形机器人研究将机器人视为双足移动操作平台，其中仅脚和手与环境接触。然而，我们人类会使用所有身体部位与世界互动，例如坐在椅子上、从地面起身或在地板上滚动。使用手脚以外的身体部位与环境接触，给模型预测控制和基于强化学习的方法带来了巨大挑战。不可预测的接触序列使得模型预测控制几乎无法实时提前规划。人形机器人零样本 sim-to-real 强化学习方法的成功，在很大程度上依赖于基于 GPU 的刚体物理模拟器的加速以及碰撞检测的简化。人形研究中缺乏极端躯干运动，使得所有其他组件的设计变得复杂，例如终止条件、运动指令和奖励设计。为了应对这些潜在挑战，我们提出了一种通用的人形运动框架，该框架接收离散运动指令并实时控制机器人的电机动作。使用 GPU 加速的刚体模拟器，我们训练了一种人形全身控制策略，即使在存在随机接触、极大的机器人基座旋转以及不太可行的运动指令的情况下，该策略也能在现实世界中实时遵循高级运动指令。更多详情请见 https://project-instinct.github.io
英文摘要：
Previous humanoid robot research works treat the robot as a bipedal mobile manipulation platform, where only the feet and hands contact the environment. However, we humans use all body parts to interact with the world, e.g., we sit in chairs, get up from the ground, or roll on the floor. Contacting the environment using body parts other than feet and hands brings significant challenges in both model-predictive control and reinforcement learning-based methods. An unpredictable contact sequence makes it almost impossible for model-predictive control to plan ahead in real time. The success of the zero-shot sim-to-real reinforcement learning method for humanoids heavily depends on the acceleration of GPU-based rigid-body physical simulator and simplification of the collision detection. Lacking extreme torso movement of the humanoid research makes all other components non-trivial to design, such as termination conditions, motion commands and reward designs. To address these potential challenges, we propose a general humanoid motion framework that takes discrete motion commands and controls the robot's motor action in real time. Using a GPU-accelerated rigid-body simulator, we train a humanoid whole-body control policy that follows the high-level motion command in the real world in real time, even with stochastic contacts and extremely large robot base rotation and not-so-feasible motion command. More details at https://project-instinct.github.io
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
