Learning to enhance multi-legged robot on rugged landscapes
- URL: http://arxiv.org/abs/2409.09473v1
- Date: Sat, 14 Sep 2024 15:53:08 GMT
- Title: Learning to enhance multi-legged robot on rugged landscapes
- Authors: Juntao He, Baxi Chong, Zhaochen Xu, Sehoon Ha, Daniel I. Goldman,
- Abstract summary: Multi-legged robots offer a promising solution forNavigating rugged landscapes.
Recent studies have shown that a linear controller can ensure reliable mobility on challenging terrains.
We develop a MuJoCo-based simulator tailored to this robotic platform and use the simulation to develop a reinforcement learning-based control framework.
- Score: 7.956679144631909
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Navigating rugged landscapes poses significant challenges for legged locomotion. Multi-legged robots (those with 6 and greater) offer a promising solution for such terrains, largely due to their inherent high static stability, resulting from a low center of mass and wide base of support. Such systems require minimal effort to maintain balance. Recent studies have shown that a linear controller, which modulates the vertical body undulation of a multi-legged robot in response to shifts in terrain roughness, can ensure reliable mobility on challenging terrains. However, the potential of a learning-based control framework that adjusts multiple parameters to address terrain heterogeneity remains underexplored. We posit that the development of an experimentally validated physics-based simulator for this robot can rapidly advance capabilities by allowing wide parameter space exploration. Here we develop a MuJoCo-based simulator tailored to this robotic platform and use the simulation to develop a reinforcement learning-based control framework that dynamically adjusts horizontal and vertical body undulation, and limb stepping in real-time. Our approach improves robot performance in simulation, laboratory experiments, and outdoor tests. Notably, our real-world experiments reveal that the learning-based controller achieves a 30\% to 50\% increase in speed compared to a linear controller, which only modulates vertical body waves. We hypothesize that the superior performance of the learning-based controller arises from its ability to adjust multiple parameters simultaneously, including limb stepping, horizontal body wave, and vertical body wave.
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