[Submitted on 20 Jul 2026]
Title:Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion
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Abstract:Reinforcement learning (RL) for legged robots is advancing locomotion, demonstrating its ability to adapt to new and challenging terrain. Traditionally, these RL locomotion frameworks are position-based, making the policy less adaptable to terrain types and requiring state estimation techniques in the observation space, i.e., linear velocity. Moreover, these RL frameworks often use small, lightweight quadrupeds that are limited in their viability for high-complexity tasks due to hardware constraints. This work explores an RL torque control framework for heavyweight high-torque quadrupeds. The RL framework in this paper can traverse rough terrain and effectively track a desired linear velocity without requiring knowledge of the agent's current velocity. Using Nvidia's Isaac Sim and Isaac Lab, simulation results of the RL torque control policy are shown on the Unitree B1 quadruped, achieving speeds of 3.5 m/s and 1.5 rad/s. In addition, the quadruped can walk up and down stairs without the aid of an exteroceptive sensor.
Comments: 6 pages, 4 figures. Accepted manuscript. Published in the 2026 IEEE/SICE International Symposium on System Integration (SII), pp. 1259-1264
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2607.18365 [cs.RO]
(or arXiv:2607.18365v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18365
arXiv-issued DOI via DataCite
Journal reference: 2026 IEEE/SICE International Symposium on System Integration (SII), pp. 1259-1264 (2026)
Related DOI:
https://doi.org/10.1109/SII64115.2026.11404550
DOI(s) linking to related resources
Submission history
From: Jordan Dowdy [view email] [v1] Mon, 20 Jul 2026 16:35:53 UTC (617 KB)
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