Safe RL Policy Optimization

[Ongoing Project] Learning model-free reinforcement learning policies with internal safety and stability guarantees mainly for manipulation and locomotion tasks. The experiments leverage domain randomization and sim-to-real capabilities of Isaac Sim and Isaac Lab simulators. The project is in collaboration with Mitacs, funded by the Mitacs Accelerate Fellowship and an industrial partner, Sycodal Electronics Inc.