Movie S2
Stair-climbing race between DreamWaQ++, DreamWaQ, and the built-in controller.
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.
Stair-climbing race between DreamWaQ++, DreamWaQ, and the built-in controller.
Resilient stair-climbing on various stairs with different rise and run propoerties.
Emergent probing behavior on uncertain terrains.
Adaptation in out-of distribution situations.
Locomotion performance comparison on extreme slopes.
Scalability of DreamWaQ++ on various robots.
Demonstration of DreamWaQ++ on extreme locomotion tasks such as overcoming high obstacles.
Comparison of the baseline DreamWaQ on different robot platforms.
In-depth experiment using of Movie S2 with asynchronous race.
@article{nahrendra2024obstacle,
title={Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning},
author={Nahrendra, I and Yu, Byeongho and Oh, Minho and Lee, Dongkyu and Lee, Seunghyun and Lee, Hyeonwoo and Lim, Hyungtae and Myung, Hyun},
journal={arXiv preprint arXiv:2409.19709},
year={2024}
}