让机器人更智能地抓捏布料，出错能自动修复
Phase-Conditioned Imitation Learning with Autonomous Failure Recovery for Robust Deformable Object Manipulation
- 用任务阶段条件控制动作，避免视觉相似时误判
- 实测吊衣成功率从56%提升至87%，支持自动纠错
- 适合做柔性物体操作的机器人研究者参考
本文提出一种相位条件化、力觉感知的鲁棒柔性物体操作框架。标准模仿学习策略（如基于Transformer的动作分块）在推理时依赖马尔可夫假设，导致视觉相似状态需不同动作时产生状态混淆，并无法自主恢复执行失败。为此，我们设计闭环分层架构：一个受FiLM调节的ACT编码器根据当前任务阶段动态调整特征提取，使单一统一策略能生成阶段特定行为，同时共享跨阶段动作动态；一个多模态相位预测器融合视觉、力觉与位姿反馈，实时估计任务阶段，检测仅靠视觉无法识别的接触失败，并自主触发恢复轨迹。系统还包含混合阻抗控制器实现柔顺执行，以及触觉遥操作界面用于力觉数据采集。消融实验表明，基于FiLM的调制显著优于无条件及词元级条件基线，t-SNE分析证实其生成了清晰分离的阶段特异性特征表示。在双臂悬挂和取下T恤任务上验证，闭环系统将悬挂成功率从56%提升至87%。代码与视频：https://leledeyuan00.github.io/phaser/
原文摘要 · Abstract (English)
This paper presents a phase-conditioned, force-aware framework for robust deformable object manipulation. Standard imitation learning policies such as Action Chunking with Transformers (ACT) rely on a Markovian assumption at inference, causing state aliasing when visually similar observations require contradictory actions and preventing autonomous recovery from execution failures. We address this with a closed-loop hierarchical architecture. A FiLM-conditioned ACT encoder modulates feature extraction based on the current task phase, enabling a single unified policy to produce phase-specific behaviors while sharing action dynamics across phases. A multi-modal phase predictor fusing visual, force, and pose feedback estimates the phase in real time, detecting contact failures that are invisible to vision alone and autonomously triggering recovery trajectories. The system is completed by a hybrid impedance controller for compliant execution and a haptic teleoperation interface for force-aware data collection. Ablation studies show that FiLM-based modulation significantly outperforms both unconditioned and token-level conditioned baselines, and t-SNE analysis confirms that FiLM induces well-separated, phase-specific feature representations. Validated on hanging and removing a T-shirt with dual arms, the closed-loop system improves the hanging success rate from 56\% to 87\% through autonomous error recovery. Code and videos: https://leledeyuan00.github.io/phaser/
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