arXiv cs.AI· Fanqi Yu, Matteo Tiezzi, Cigdem Beyan, Tommaso Apicella, Vittorio Murino·· 3 小时前AI 评分36
HIDRA：面向无回放终身模仿学习的分层双路由注意力框架
HIDRA: Hierarchical Dual-Routing Attention for Replay-Free Lifelong Imitation Learning
AI 导读
研究者提出 HIDRA，一种分层双注意力路由框架，用于无回放、单次数据流下的终身模仿学习。该方法将指令级专家检索与上下文相关精化解耦，并通过 key 级几何正则化稳定表征漂移下的专家选择。在 LIBERO 基准多个套件上，HIDRA 的 AUC 和前向迁移优于无回放与回放基线，同时保持低遗忘，代码已开源。
正文 · 原文
Abstract:Robotic agents operating in real-world environments must adapt continuously from a stream of multimodal demonstrations, often under constraints that preclude storing or revisiting past data. In this replay-free, single-pass lifelong imitation learning setting, sequential updates can progressively distort the representation space, making expert-based approaches particularly sensitive to routing interference, causing expert selection to degrade over time. We propose HIDRA, a structured routing framework that mitigates this issue through a hierarchical dual-attention mechanism, which decouples instruction-level expert retrieval from context-dependent refinement. To further stabilize routing under representation drift, HIDRA introduces a key-level geometric regularization that enforces both alignment within tasks and separation across tasks. The resulting approach enables reliable expert selection while supporting an expandable set of experts for continual adaptation. We evaluate HIDRA on several LIBERO benchmark suites, including more challenging variants with heterogeneous task sequences and paraphrased language instructions. Our approach consistently outperforms both replay-free and replay-based baselines, improving AUC and forward transfer while maintaining low forgetting, with stronger gains under high interference and language variation. The code is available at: this https URL.
| Comments: | This is the authors' preprint, accepted to the NeurIPS 2026 Conference. The final version is available in the NeurIPS 2026 proceedings |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36374 [cs.RO] |
| (or arXiv:2610.36374v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36374 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Cigdem Beyan [view email]
[v1]
Mon, 26 Oct 2026 22:43:30 UTC (973 KB)
来源：arXiv cs.AI · arxiv.org