WoCoCo：学习具有顺序接触的全身人形控制
WoCoCo: Learning Whole-Body Humanoid Control with Sequential Contacts
AI总结：
WoCoCo通过将任务分解为接触阶段，实现了无模型强化学习的全身人形顺序接触控制，无需运动先验，并在现实世界中完成了跑酷跳跃、箱子操作、舞蹈和攀爬等任务，且可推广至其他机器人。
摘要：
涉及顺序接触的人形活动对于现实世界中复杂的机器人交互和操作至关重要，传统上通过基于模型的运动规划来解决，这耗时且通常依赖于简化的动力学模型。尽管无模型强化学习（RL）已成为多功能且鲁棒的全身人形控制的强大工具，但它仍然需要繁琐的任务特定调整和状态机设计，并且在涉及接触序列的任务中遭受长视界探索问题。在这项工作中，我们提出了WoCoCo（全身控制与顺序接触），一个统一的框架，通过将任务自然地分解为单独的接触阶段来学习具有顺序接触的全身人形控制。这种分解通过任务无关的奖励和仿真到现实的设计促进了简单且通用的策略学习流程，每个任务只需指定一两个与任务相关的术语。我们证明了使用WoCoCo训练的端到端RL控制器能够在现实世界中实现四个涉及不同接触序列的具有挑战性的全身人形任务，而无需任何运动先验：1）多才多艺的跑酷跳跃，2）箱子移动操作，3）动态拍手和跺脚舞蹈，以及4）悬崖攀爬。我们进一步展示了WoCoCo是一个超越人形的通用框架，通过将其应用于22自由度恐龙机器人移动操作任务。
英文摘要：
Humanoid activities involving sequential contacts are crucial for complex robotic interactions and operations in the real world and are traditionally solved by model-based motion planning, which is time-consuming and often relies on simplified dynamics models. Although model-free reinforcement learning (RL) has become a powerful tool for versatile and robust whole-body humanoid control, it still requires tedious task-specific tuning and state machine design and suffers from long-horizon exploration issues in tasks involving contact sequences. In this work, we propose WoCoCo (Whole-Body Control with Sequential Contacts), a unified framework to learn whole-body humanoid control with sequential contacts by naturally decomposing the tasks into separate contact stages. Such decomposition facilitates simple and general policy learning pipelines through task-agnostic reward and sim-to-real designs, requiring only one or two task-related terms to be specified for each task. We demonstrated that end-to-end RL-based controllers trained with WoCoCo enable four challenging whole-body humanoid tasks involving diverse contact sequences in the real world without any motion priors: 1) versatile parkour jumping, 2) box loco-manipulation, 3) dynamic clap-and-tap dancing, and 4) cliffside climbing. We further show that WoCoCo is a general framework beyond humanoid by applying it in 22-DoF dinosaur robot loco-manipulation tasks.
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。
