Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations

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Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations

Acero, F; Yuan, K; Li, Z; (2022) Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations. IEEE Robotics and Automation Letters , 7 (4) pp. 8611-8618. 10.1109/LRA.2022.3188108. Green open access

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Abstract

To proactively navigate and traverse various terrains, active use of visual perception becomes indispensable. We aim to investigate the feasibility and performance of using sparse visual observations to achieve perceptual locomotion over a range of common terrains (steps, ramps, gaps, and stairs) in human-centered environments. We formulate a selection of sparse visual inputs suitable for locomotion over the terrains of interest, and propose a learning framework to integrate exteroceptive and proprioceptive states. We design state observations and a training curriculum to learn feedback control policies effectively over a range of different terrains. We extensively validate and benchmark the learned policy in various tasks: omnidirectional walking on flat ground, and forward locomotion over various obstacles, showing high success rate of traversability. Furthermore, we study exteroceptive ablations and evaluate policy generalization by adding various levels of noise and testing on new unseen terrains. We demonstrate the capabilities of autonomous perceptual locomotion that can be achieved by <italic>only</italic> using sparse visual observations from direct depth measurements, which are easily available from a Lidar or RGB-D sensor, showing robust ascent and descent over high stairs of 20 cm height, i.e., 50% leg length, and robustness against noise and unseen terrains.

Type:	Article
Title:	Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1109/LRA.2022.3188108
Publisher version:	https://doi.org/10.1109/LRA.2022.3188108
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Science & Technology, Technology, Robotics, Legged locomotion, robot learning, robot vision systems
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10159087