用运动先验实现人形与四足机器人高效协同移动抓取
Latent Conditioned Loco-Manipulation Using Motion Priors
- 通过模仿合成或重构的狗运动，训练通用动作策略并控制隐空间
- 在H1和Solo12机器人上实现仿真与硬件部署的移动抓取任务
- 引入扩散判别器提升模仿质量，支持约束处理保障部署安全
尽管人形和四足机器人具备广泛能力，但当前深度强化学习方法多聚焦单一技能，难以应对需综合高阶目标、物理限制与期望运动风格的复杂任务。本文提出先通过模仿学习训练通用动作策略，获得低层技能并支持隐空间控制，再用于高效求解下游任务。该方法已在计算机图形角色控制中取得成功。本文将其应用于人形与四足机器人的协同移动抓取，模仿简单合成运动或经过运动学重定向的狗运动。我们扩展原始框架以处理约束，确保部署安全性，并采用扩散判别器提升模仿质量。在H1人形机器人和Solo12四足机器人上完成仿真测试，并在Solo12硬件上成功部署。相关视频与代码见https://gepetto.github.io/LaCoLoco/
原文摘要 · Abstract (English)
Although humanoid and quadruped robots provide a wide range of capabilities, current control methods, such as Deep Reinforcement Learning, focus mainly on single skills. This approach is inefficient for solving more complicated tasks where high-level goals, physical robot limitations and desired motion style might all need to be taken into account. A more effective approach is to first train a multipurpose motion policy that acquires low-level skills through imitation, while providing latent space control over skill execution. Then, this policy can be used to efficiently solve downstream tasks. This method has already been successful for controlling characters in computer graphics. In this work, we apply the approach to humanoid and quadrupedal loco-manipulation by imitating either simple synthetic motions or kinematically retargeted dog motions. We extend the original formulation to handle constraints, ensuring deployment safety, and use a diffusion discriminator for better imitation quality. We verify our methods by performing loco-manipulation in simulation for the H1 humanoid and Solo12 quadruped, as well as deploying policies on Solo12 hardware. Videos and code are available at https://gepetto.github.io/LaCoLoco/
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