让机器人更懂任务本质，减少环境干扰，提升操作成功率。
OminiAdapt: Learning Cross-Task Invariance for Robust and Environment-Aware Robotic Manipulation
- 聚焦任务目标，用注意力机制过滤背景信息
- 动态权重更新使成功率显著提升，跨场景表现稳健
- 适合需要强适应性的高精度人形机器人任务
随着具身智能的快速发展，利用大规模人类数据进行人形机器人高层模仿学习已成为学术界与产业界的关注焦点。然而，由于感知与控制过程复杂、人形机器人与人类在形态和驱动机制上长期存在差异，以及从第一视角视觉中难以获取任务相关特征，将人形机器人应用于精密操作领域仍面临挑战。为解决模仿学习中的协变量偏移问题，本文提出一种专为人形机器人设计的模仿学习算法。通过聚焦主要任务目标，过滤背景信息，并结合通道特征融合与空间注意力机制，抑制环境干扰；同时采用动态权重更新策略，显著提升人形机器人完成目标任务的成功率。实验结果表明，该方法在多种典型任务场景中均表现出良好的鲁棒性与可扩展性，为实现人形机器人的自主学习与控制提供了新思路。项目代码将开源至GitHub。
原文摘要 · Abstract (English)
With the rapid development of embodied intelligence, leveraging large-scale human data for high-level imitation learning on humanoid robots has become a focal point of interest in both academia and industry. However, applying humanoid robots to precision operation domains remains challenging due to the complexities they face in perception and control processes, the long-standing physical differences in morphology and actuation mechanisms between humanoid robots and humans, and the lack of task-relevant features obtained from egocentric vision. To address the issue of covariate shift in imitation learning, this paper proposes an imitation learning algorithm tailored for humanoid robots. By focusing on the primary task objectives, filtering out background information, and incorporating channel feature fusion with spatial attention mechanisms, the proposed algorithm suppresses environmental disturbances and utilizes a dynamic weight update strategy to significantly improve the success rate of humanoid robots in accomplishing target tasks. Experimental results demonstrate that the proposed method exhibits robustness and scalability across various typical task scenarios, providing new ideas and approaches for autonomous learning and control in humanoid robots. The project will be open-sourced on GitHub.
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