外观
论文arXiv 2610.1483318 天前
将潜在运动适应扩展到协调双臂操作
研究用基于交互历史的潜在条件化将RMA扩展到双臂操作，并在ManiSkill任务和双Franka真机举升中验证。
Franka FR3原文链接：Scaling Latent Motor Adaptation to Coordinated Bimanual Manipulation（arXiv 2610.14833 · 发布日期：2026-10-10）
Scaling Latent Motor Adaptation to Coordinated Bimanual Manipulation
研究用基于交互历史的潜在条件化将RMA扩展到双臂操作，并在ManiSkill任务和双Franka真机举升中验证。
核心要点
- 物体质量、摩擦、尺度或形状变化会使固定假设策略退化。
- 双臂RMA以观测-动作历史和双腕部深度图推断潜在动态。
- 在双臂抓取与举升中保持特权策略性能，静态PPO成功率低。
- 无需真实微调即可在双Franka举升任务直接Sim-to-Real迁移。
验证范围与局限：验证范围包括三个ManiSkill接触丰富任务和一个双Franka举升真机任务；长时程堆叠仍有限。
英文摘要 (Abstract)
Robust manipulation under uncertain object dynamics and geometry remains a core challenge in real-world robotics. Policies trained under fixed assumptions often degrade when object mass, friction, scale, or shape vary. Embedding-based adaptation methods such as Rapid Motor Adaptation (RMA) address this by conditioning actions on latent representations of environment dynamics inferred from interaction history. We investigate whether this paradigm scales from single arm settings to coordinated bimanual manipulation, where higher dimensional control, coupled contacts, and shared object forces introduce additional complexity. We evaluate bimanual RMA on three contact-rich ManiSkill tasks with randomized YCB object properties. Bimanual RMA, using observation-action history and dual wrist depth images, retains privileged policy performance on bimanual picking and lifting, while an otherwise matched stationary PPO policy without latent conditioning achieves low success; all methods remain limited on long-horizon stacking. We further demonstrate direct sim-to-real transfer on a dual Franka lifting task without real-world finetuning. Together, these results suggest that latent conditioning provides useful structure for scaling robust control to coordinated bimanual systems, while longer horizon tasks require additional temporal structure.
作者：Seung-Bin Joo, Alessandro Albini, Luca Wiehe, Perla Maiolino, Daniel Olmeda Reino, João F. Henriques
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