Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion
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 velo...
| Authors | Jordan Dowdy, Jean Chagas Vaz |
|---|---|
| Primary category | cs.RO |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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