Robot Cognitive Learning by Considering Physical Properties

Fuchun Sun, Wenbing Huang, Yu Luo, Tianying Ji, Huaping Liu, He Liu, Jianwei Zhang

Engineering ›› 2025, Vol. 47 ›› Issue (4) : 168-179.

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Engineering ›› 2025, Vol. 47 ›› Issue (4) : 168-179. DOI: 10.1016/j.eng.2024.10.013
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Robot Cognitive Learning by Considering Physical Properties

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Abstract

Humans achieve cognitive development through continuous interaction with their environment, enhancing both perception and behavior. However, current robots lack the capacity for human-like action and evolution, posing a bottleneck to improving robotic intelligence. Existing research predominantly models robots as one-way, static mappings from observations to actions, neglecting the dynamic processes of perception and behavior. This paper introduces a novel approach to robot cognitive learning by considering physical properties. We propose a theoretical framework wherein a robot is conceptualized as a three-body physical system comprising a perception-body (P-body), a cognition-body (C-body), and a behavior-body (B-body). Each body engages in physical dynamics and operates within a closed-loop interaction. Significantly, three crucial interactions connect these bodies. The C-body relies on the P-body’s extracted states and reciprocally offers long-term rewards, optimizing the P-body’s perception policy. In addition, the C-body directs the B-body’s actions through sub-goals, and subsequent P-body-derived states facilitate the C-body’s cognition dynamics learning. At last, the B-body would follow the sub-goal generated by the C-body and perform actions conditioned on the perceptive state from the P-body, which leads to the next interactive step. These interactions foster the joint evolution of each body, culminating in optimal design. To validate our approach, we employ a navigation task using a four-legged robot, D’Kitty, equipped with a movable global camera. Navigational prowess demands intricate coordination of sensing, planning, and D’Kitty’s motion. Leveraging our framework yields superior task performance compared with conventional methodologies. In conclusion, this paper establishes a paradigm shift in robot cognitive learning by integrating physical interactions across the P-body, C-body, and B-body, while considering physical properties. Our framework’s successful application to a navigation task underscores its efficacy in enhancing robotic intelligence.

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Robot learning / Physical basis / Cognitive learning

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Fuchun Sun, Wenbing Huang, Yu Luo, Tianying Ji, Huaping Liu, He Liu, Jianwei Zhang. Robot Cognitive Learning by Considering Physical Properties. Engineering, 2025, 47(4): 168‒179 https://doi.org/10.1016/j.eng.2024.10.013

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