Reconfigurable Robot Identification from Motion Data
<|reference_start|>Reconfigurable Robot Identification from Motion Data: Integrating Large Language Models (VLMs) and Vision-Language Models (VLMs) with robotic systems enables robots to process and understand complex natural language instructions and visual information. However, a fundamental challenge remains: for robots to fully capitalize on these advancements, they must have a deep understanding of their physical embodiment. The gap between AI models cognitive capabilities and the understanding of physical embodiment leads to the following question: Can a robot autonomously understand and adapt to its physical form and functionalities through interaction with its environment? This question underscores the transition towards developing self-modeling robots without reliance on external sensory or pre-programmed knowledge about their structure. Here, we propose a meta self modeling that can deduce robot morphology through proprioception (the internal sense of position and movement). Our study introduces a 12 DoF reconfigurable legged robot, accompanied by a diverse dataset of 200k unique configurations, to systematically investigate the relationship between robotic motion and robot morphology. Utilizing a deep neural network model comprising a robot signature encoder and a configuration decoder, we demonstrate the capability of our system to accurately predict robot configurations from proprioceptive signals. This research contributes to the field of robotic self-modeling, aiming to enhance understanding of their physical embodiment and adaptability in real world scenarios.<|reference_end|>
@article{hu2024reconfigurable,
    title={Reconfigurable Robot Identification from Motion Data},
    author={Yuhang Hu, Yunzhe Wang, Ruibo Liu, Zhou Shen, Hod Lipson},
    journal={arXiv preprint arXiv:2403.10496},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10496},
    primaryClass={cs.RO}
}