通过进度率与关键帧记忆，提升长时复杂抓取任务的稳定控制。
Bi-HIL: Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulation
- 用子任务进度率和关键帧记忆协调高低层策略
- 实机测试中显著优于平铺策略与消融版本
- 适合需要力觉反馈的长时序机器人操作场景
长时序、高接触交互的机器人操作因观测不全和接触不确定性导致子任务切换不稳定而难以实现。尽管分层架构能增强时间推理能力，双侧模仿学习可实现力觉感知控制，但现有方法多依赖扁平策略，难以处理长时序协同。本文提出Bi-HIL，一种基于双侧控制的多模态分层模仿学习框架，通过将关键帧记忆与子任务级进度率结合，建模活跃子任务内的阶段进展，并同时调控高低层策略。在单臂与双臂真实机器人任务上评估表明，Bi-HIL相较平铺策略及消融变体均有稳定提升。结果凸显显式建模子任务进展与力觉控制对鲁棒长时序操作的重要性。
原文摘要 · Abstract (English)
Long-horizon contact-rich robotic manipulation remains challenging due to partial observability and unstable subtask transitions under contact uncertainty. While hierarchical architectures improve temporal reasoning and bilateral imitation learning enables force-aware control, existing approaches often rely on flat policies that struggle with long-horizon coordination. We propose Bi-HIL, a bilateral control-based multimodal hierarchical imitation learning framework for long-horizon manipulation. Bi-HIL stabilizes hierarchical coordination by integrating keyframe memory with subtask-level progress rate that models phase progression within the active subtask and conditions both high- and low-level policies. We evaluate Bi-HIL on unimanual and bimanual real-robot tasks, demonstrating consistent improvements over flat and ablated variants. The results highlight the importance of explicitly modeling subtask progression together with force-aware control for robust long-horizon manipulation. For additional material, please check: https://mertcookimg.github.io/bi-hil
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