Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning
<|reference_start|>Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning: Quadrupedal robots hold promising potential for applications in navigating cluttered environments with resilience akin to their animal counterparts. However, their floating base configuration makes them vulnerable to real-world uncertainties, yielding substantial challenges in their locomotion control. Deep reinforcement learning has become one of the plausible alternatives for realizing a robust locomotion controller. However, the approaches that rely solely on proprioception sacrifice collision-free locomotion because they require front-feet contact to detect the presence of stairs to adapt the locomotion gait. Meanwhile, incorporating exteroception necessitates a precisely modeled map observed by exteroceptive sensors over a period of time. Therefore, this work proposes a novel method to fuse proprioception and exteroception featuring a resilient multi-modal reinforcement learning. The proposed method yields a controller that showcases agile locomotion performance on a quadrupedal robot over a myriad of real-world courses, including rough terrains, steep slopes, and high-rise stairs, while retaining its robustness against out-of-distribution situations.<|reference_end|>
@article{nahrendra2024obstacle-aware,
    title={Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal
  Reinforcement Learning},
    author={I Made Aswin Nahrendra, Byeongho Yu, Minho Oh, Dongkyu Lee, Seunghyun
  Lee, Hyeonwoo Lee, Hyungtae Lim, Hyun Myung},
    journal={arXiv preprint arXiv:2409.19709},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19709},
    primaryClass={cs.RO cs.SY eess.SY}
}