让机器人通过多感官自监督预训练，提升复杂操作的适应力和学习效率。
Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
- 用掩码自编码训练跨模态感知，融合视觉、触觉与本体感觉
- 真实机器人仅需6000次交互即达高成功率，抗传感器噪声强
- 适合需要高鲁棒性的物理机器人操控任务，如抓取、装配
有效的接触丰富操作需要机器人协同利用视觉、力觉和本体感知。然而，强化学习代理在多感官环境中学习困难，尤其面对感官噪声和动态变化。我们提出多感官动态预训练（MSDP），一种专为任务导向策略学习设计的表达性多感官表征学习框架。MSDP基于掩码自编码，通过仅使用部分传感器嵌入重建多感官观测，实现跨模态预测与传感器融合。下游策略学习中，我们引入新型非对称架构：批评者通过交叉注意力从冻结嵌入中提取动态任务特征，而执行者接收稳定池化表示以指导动作。实验表明，该方法显著加速学习并具备强鲁棒性，可在多种挑战性接触丰富任务中应对传感器噪声与物体动力学变化。仿真与真实世界测试均验证其有效性。该方法在真实机器人上仅需6000次在线交互即可实现高成功率，为复杂多感官机器人控制提供简单而强大的解决方案。
原文摘要 · Abstract (English)
Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Learning agents struggle to learn in such multisensory settings, especially amidst sensory noise and dynamic changes. We propose MultiSensory Dynamic Pretraining (MSDP), a novel framework for learning expressive multisensory representations tailored for task-oriented policy learning. MSDP is based on masked autoencoding and trains a transformer-based encoder by reconstructing multisensory observations from only a subset of sensor embeddings, leading to cross-modal prediction and sensor fusion. For downstream policy learning, we introduce a novel asymmetric architecture, where a cross-attention mechanism allows the critic to extract dynamic, task-specific features from the frozen embeddings, while the actor receives a stable pooled representation to guide its actions. Our method demonstrates accelerated learning and robust performance under diverse perturbations, including sensor noise, and changes in object dynamics. Evaluations in multiple challenging, contact-rich robot manipulation tasks in simulation and the real world showcase the effectiveness of MSDP. Our approach exhibits strong robustness to perturbations and achieves high success rates on the real robot with as few as 6,000 online interactions, offering a simple yet powerful solution for complex multisensory robotic control. Website: https://msdp-pearl.github.io/
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