发表机构
University of Freiburg（弗莱堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究动态环境中双机器人操作问题，提出轻量级DynaMAC框架，将相对手臂视为动态任务参数，解决因果限制，保留多流策略优势。通过DynaBench基准测试，性能远超基线，能零样本泛化，简化数据收集，为人机协作助力。
AI 中文摘要
多流机器人操作策略通过相对于环境参考框架对动作进行建模，实现了无与伦比的样本效率和泛化能力。然而，现有方法通常假设这些框架是严格外生的。这种因果假设在动态环境中会失效，比如单个机器人手臂操作移动物体或两个手臂协调时，每个手臂实际上都成为了另一个手臂动态环境的一部分。我们提出了DynaMAC，这是一个轻量级、与策略无关的框架，它在保留多流策略的样本效率、计算速度和灵活性的同时，解决了这一因果限制。DynaMAC将相对的手臂视为动态任务参数，从而为动态操作和双机器人协调提供了统一的公式，而无需明确的主从关系。为了严格评估这些能力，我们引入了DynaBench，这是一个用于动态环境中机器人操作的新型基准。在动态环境和双机器人操作任务中，DynaMAC的性能比领先的概率性和生成性基线高出35个百分点以上，同时所需样本减少20倍。至关重要的是，DynaMAC能够从静态演示中零样本泛化到动态环境，大大简化了数据收集，并为人机协作搭建了一座优雅的桥梁。
英文摘要
Multi-stream robot manipulation policies achieve unparalleled sample efficiency and generalization by modeling actions relative to environmental reference frames. However, existing approaches typically assume these frames to be strictly exogenous. This causal assumption collapses in dynamic settings, such as when a single robot arm manipulates a moving object or when two arms coordinate, where each arm effectively becomes part of the dynamic environment of the other. We propose DynaMAC, a lightweight, policy-agnostic framework that resolves this causal limitation while preserving the sample efficiency, computational speed, and flexibility of multi-stream policies, DynaMAC treats the opposite arm as a dynamic task parameter, thereby providing a unified formulation for dynamic manipulation and bimanual coordination without requiring an explicit leader-follower relationship. To rigorously evaluate these capabilities, we introduce DynaBench, a novel benchmark for robot manipulation in dynamic environments. Across both dynamic environments and bimanual manipulation tasks, DynaMAC outperforms leading probabilistic and generative baselines by over 35 percentage points while requiring 20 times fewer samples. Crucially, DynaMAC generalizes zero-shot from static demonstrations to dynamic environments, substantially simplifying data collection and establishing an elegant bridge toward human-robot collaboration.
