RL-Based Quadruped Locomotion Systems
Internal deployment work on RL locomotion controllers, sim-to-real training workflows, and safety-critical evaluation for industrial quadrupeds.
Artifact type: Internal deployment work.
Developing reinforcement-learning-based locomotion controllers in IsaacSim/IsaacLab for quadruped robots deployed in industrial inspection.
Work spans GPU-accelerated policy training, simulation infrastructure, evaluation workflows, sim-to-real iteration, and safety curricula for rare failure modes. This project is described at a high level to avoid disclosing proprietary ANYbotics details.