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Learning to walk in confined spaces using 3D representation
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Legged robots have the potential to traverse complex terrain and access confined spaces beyond the reach of traditional platforms thanks to their ability to carefully select footholds and flexibly adapt their body posture while walking. However, robust deployment in real-world applications is still an open challenge. In this paper, we present a method for legged locomotion control using reinforcement learning and 3D volumetric representations to enable robust and versatile locomotion in confined and unstructured environments. By employing a two-layer hierarchical policy structure, we exploit the capabilities of a highly robust low-level policy to follow 6D commands and a high-level policy to enable three-dimensional spatial awareness for navigating under overhanging obstacles. Our study includes the development of a procedural terrain generator to create diverse training environments. We present a series of experimental evaluations in both simulation and real-world settings, demonstrating the effectiveness of our approach in controlling a quadruped robot in confined, rough terrain. By achieving this, our work extends the applicability of legged robots to a broader range of scenarios.
Forward citations
Cited by 5 Pith papers
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Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
A single onboard policy trained with 2D trajectory-optimization priors, transformer latent actions, and reinforcement learning enables a quadruped to autonomously select gaits and traverse unstructured terrain at up to 6 m/s.
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High-speed control and navigation for quadrupedal robots on complex and discrete terrain
A hierarchical planner-plus-tracker system enables a quadruped to run on walls, clear a 1.3 m gap, and navigate discrete terrain at up to 4 m/s using a competitive generative curriculum.
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Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain
A quadruped controller estimates a sliding payload's mass, friction, position, and velocity from proprioception, then uses that estimate to stabilize the load while walking on rough terrain.
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Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface
A waypoint-based interface between planners and a trained quadrupedal locomotion policy enables navigation over diverse obstacles in simulation and on a real robot.
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ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real
An adaptive curriculum that steers a pretrained diffusion terrain generator via policy success-weighted latent blending improves zero-shot sim-to-real off-road navigation performance.
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