aiqbench analysis
This research advances legged robotics by shifting reinforcement learning from position-based control to a torque-driven approach for heavyweight quadrupeds. The framework enables high-speed traversal of rough terrain and stair negotiation without relying on external sensors or precise velocity state estimation, addressing key hardware limitations in current models. By leveraging Nvidia's simulation environments, the study demonstrates improved adaptability for complex physical tasks beyond small-scale prototypes.
Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion
arXiv:2607.18365v1 Announce Type: cross 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.
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.
6 pages, 4 figures. Accepted manuscript. Published in the 2026 IEEE/SICE International Symposium on System Integration (SII), pp. 1259-1264
Robotics (cs.RO)
arXiv:2607.18365 [cs.RO]
(or arXiv:2607.18365v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18365 Focus to learn more arXiv-issued DOI via DataCite
2026 IEEE/SICE International Symposium on System Integration (SII), pp. 1259-1264 (2026)
https://doi.org/10.1109/SII64115.2026.11404550 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 283 words
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