外观
论文arXiv 2609.288071 个月前
流式深度强化学习实现机器人自适应持续学习与在线策略更新
本文由 AI 根据公开内容整理，可能有误，请以原文为准：An Analysis of Streaming Deep Reinforcement Learning for Adaptive Continual Learning in Robotics（arXiv 2609.28807）
核心内容
针对机器人部署后遭遇未预见场景导致性能下降的问题，arXiv论文首次系统分析流式深度强化学习在机器人自适应持续学习中的有效性。研究表明，在完成初始预训练后，结合特定优化器与可塑性损失缓解技术，机器人可利用实时经验流在线适应自身、环境或目标的动态变化。在四足机器人运动控制实验中，该方法显著优于传统批次在线策略，任务成功率较纯预训练基线最高提升90%。
技术方案核心在于摒弃离线批量重训模式，仅利用最新经验流进行策略更新，模拟生物时刻学习机制以克服灾难性遗忘。实验覆盖四足运动与机械臂操作两类形态，验证了领域知识从预训练阶段向在线适应阶段的有效迁移。尽管在四足平台上取得突破性进展，但在机械臂操作任务中复现时表现出一定的稳定性与性能边界，揭示了流式RL在不同具身形态间泛化能力的差异及当前工程落地的约束条件。
关键看点
- 四足运动适配：流式深度RL使四足机器人在线适应未见变化，任务成功率较预训练策略最高提升90%
- 算法架构创新：采用特定优化器配合可塑性损失缓解技术，支持仅依赖最新经验流的实时策略更新
- 跨形态验证：在机械臂操作中部分复现四足运动的成功经验，但暴露出稳定性与性能上限问题
- 范式对比优势：作为首个机器人持续学习流式RL分析，实测性能显著优于传统批次制在线学习方法
工程与产业观察
该研究为具身智能摆脱对大规模离线数据集的依赖提供了实证路径，证明流式RL能激活预训练模型的在线适应能力。对于算法工程师，需重点关注优化器选型与抗遗忘机制设计；产业侧应评估其在非结构化环境中的长尾适应性价值，同时警惕从移动底盘向操作臂迁移时的稳定性衰减风险。
英文摘要 (Abstract)
Over the course of a lifetime, robots may encounter novel scenarios unaccounted for in its original training that result in performance degradation. One common approach to mitigating this issue is to further grow the offline training dataset in hopes of producing a policy robust to these changes. In contrast, biological learning occurs moment-to-moment via a stream of experience, unlike the predominantly batch-based and offline nature of deep learning. Although recent works show the feasibility of stream-based deep reinforcement learning, where updates use only the latest experience, none have shown it to be a viable continual learning framework for adapting robotic policies to unseen changes. In this paper, we present the first analysis of streaming deep reinforcement learning for adaptive continual learning in robotics. In particular, we show that, following an initial pretraining phase, streaming deep RL can enable a robot to successfully adapt to unforeseen changes to itself, its environment, or goals. Our primary experiments within quadruped locomotion demonstrate that a deep neural network robotic policy with certain optimizers and plasticity loss mitigation techniques can successfully leverage domain task knowledge from its pretraining to quickly adapt online to diverse changes via stream learning, outperforming batch-based on-policy methods and improving task success rates by up to 90% over the pretrained policy. Furthermore, we perform additional evaluations on robotic manipulation tasks to determine if our previous observations extend to different robotic morphologies and scenarios. Our results show that the successes observed in quadruped locomotion can be partially realized in manipulation with stability and performance limitations. We conclude with a discussion on the limitations of our work and its implications for the future of continual robot learning.
作者：Teeratham Vitchutripop, Alyssa Quarles, Wenhe Zhang, Richard Xue, Daniel Rakita
来源
本页事实对应的公开页面，以原文为准。