用因果强化学习让机器人在未知环境更快学会判断物体能否移动。
Causal Reinforcement Learning for Optimisation of Robot Dynamics in Unknown Environments
- 通过视觉特征推断物体交互的因果关系，提升决策能力。
- 复杂场景下学习时间减少超24.5%，优于传统非因果模型。
- 适合需要快速适应新环境的救援机器人等实际应用。
机器人在未知环境中自主作业面临挑战，主要源于对物体交互动力学（如可移动性）缺乏先验知识。本文提出一种新型因果强化学习方法，用于提升机器人在城市搜救（SAR）场景下的操作性能。所提机器学习架构使机器人能够学习物体视觉特征（如纹理、形状）与其交互动力学（如可移动性）之间的因果关系，显著改善决策过程。我们进行了因果发现与强化学习实验，结果表明该方法在复杂情境下学习时间缩短超过24.5%，显著优于非因果模型。
原文摘要 · Abstract (English)
Autonomous operations of robots in unknown environments are challenging due to the lack of knowledge of the dynamics of the interactions, such as the objects' movability. This work introduces a novel Causal Reinforcement Learning approach to enhancing robotics operations and applies it to an urban search and rescue (SAR) scenario. Our proposed machine learning architecture enables robots to learn the causal relationships between the visual characteristics of the objects, such as texture and shape, and the objects' dynamics upon interaction, such as their movability, significantly improving their decision-making processes. We conducted causal discovery and RL experiments demonstrating the Causal RL's superior performance, showing a notable reduction in learning times by over 24.5% in complex situations, compared to non-causal models.
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