发表机构
Australian Institute for Machine Learning (AIML), Adelaide University（澳大利亚机器学习研究所（AIML），阿德莱德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在动作分块中ACT的力感知是否依赖遥操作隐式线索，引入以观察为中心的变体，评估简单关节扭矩代理。去除隐式线索致力关键阶段失败，扭矩增强策略可恢复接触行为并改进基础策略，证明隐式线索是力感知可恢复来源。
AI 中文摘要
富含接触的操纵要求策略从仅通过视觉和运动学通常难以观察到的信号中推断交互状态。基于Transformer的动作分块（ACT）在细粒度操纵中表现出色，但许多部署通过主从遥操作收集演示，其中命令的主运动和执行的从运动之间的跟踪误差隐式编码了接触、阻力和约束违反。本文研究ACT的明显力感知是否依赖于这种隐藏的交互线索。我们引入了一个以观察为中心的ACT变体，它预测未来的从关节状态而不是主命令，从而消除遥操作引起的差异信号，同时保留学习管道的其余部分。然后，我们评估从车载电机电流或关节力导出的简单关节扭矩代理是否可以在没有外力/扭矩传感器的情况下恢复接触感知行为。在跨越表面跟随、插入、刚度辨别和基于力的停止的四个实际任务中，去除隐式线索会导致力关键阶段的严重失败。相比之下，扭矩增强策略恢复了强大的接触行为并改进了基础ACT策略。这些结果表明，在实际硬件上，隐式遥操作线索是力感知的可恢复来源，在有扭矩信号可用的情况下，简单代理与之匹配、超越或进一步增强它。
英文摘要
Contact-rich manipulation requires policies to infer interaction state from signals that are often weakly observable through vision and kinematics alone. Action Chunking with Transformers (ACT) has shown strong performance in fine-grained manipulation, but many deployments collect demonstrations through leader-follower teleoperation, where tracking error between commanded leader motion and executed follower motion implicitly encodes contact, resistance, and constraint violation. This paper examines whether ACT's apparent force-awareness depends on this hidden interaction cue. We introduce an observation-centric ACT variant that predicts future follower joint states instead of leader commands, thereby removing the teleoperation-induced discrepancy signal while preserving the rest of the learning pipeline. We then evaluate whether simple joint-torque proxies, derived from onboard motor current or joint effort, can recover contact-aware behavior without external force/torque sensors. Across four real-world tasks spanning surface following, insertion, stiffness discrimination, and force-based stopping, removing the implicit cue leads to severe failures in force-critical phases. In contrast, torque-augmented policies recover robust contact behavior and improve the base ACT policy. These results demonstrate that, on real hardware, the implicit teleoperation cue is a recoverable source of force-awareness, where torque signals are available, a simple proxy matches, surpasses, or further enhances it.
CommentsAccepted to IROS 2026
