用接触目标统一控制机器人行走与操作，一策多能。
Learning to Act Through Contact: A Unified View of Multi-Task Robot Learning
- 以接触位置、时机和末端执行器定义任务，统一多任务学习
- 单个策略在四足、人形机器人上实现多种步态与双臂操作
- 显式接触建模提升泛化能力，适合复杂机器人系统研发
我们提出一种基于显式接触表示的多任务运动与操作策略学习统一框架。不为不同任务设计独立策略，而是通过接触目标序列——期望的接触位置、时机及活跃末端执行器——统一任务定义。这使多样化的高接触任务共享结构，从而训练出可执行广泛任务的单一策略。具体地，我们采用目标条件强化学习（RL）策略实现给定接触计划。在多个机器人平台和任务上验证：四足机器人执行多种步态，人形机器人完成双足与四足步态，以及人形机器人执行不同的双臂物体操作任务。每个场景均由同一策略控制，展现跨形态系统的多样化且鲁棒的行为表现。结果表明，显式接触推理显著提升对未见场景的泛化能力，使显式接触策略学习成为可扩展协同运动与操作的有前景基础。视频见：https://youtu.be/idHx67oHHU0?si=qZJ7C0ujemXNWgA5
原文摘要 · Abstract (English)
We present a unified framework for multi-task locomotion and manipulation policy learning grounded in a contact-explicit representation. Instead of designing different policies for different tasks, our approach unifies the definition of a task through a sequence of contact goals--desired contact positions, timings, and active end-effectors. This enables leveraging the shared structure across diverse contact-rich tasks, leading to a single policy that can perform a wide range of tasks. In particular, we train a goal-conditioned reinforcement learning (RL) policy to realise given contact plans. We validate our framework on multiple robotic embodiments and tasks: a quadruped performing multiple gaits, a humanoid performing multiple biped and quadrupedal gaits, and a humanoid executing different bimanual object manipulation tasks. Each of these scenarios is controlled by a single policy trained to execute different tasks grounded in contacts, demonstrating versatile and robust behaviours across morphologically distinct systems. Our results show that explicit contact reasoning significantly improves generalisation to unseen scenarios, positioning contact-explicit policy learning as a promising foundation for scalable loco-manipulation. Video available at: https://youtu.be/idHx67oHHU0?si=qZJ7C0ujemXNWgA5
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