ntnu-arl/olympus_lab

Olympus-Lab provides Isaac Lab reinforcement-learning environments for quadruped walking, horizontal and vertical jumping, and in-flight attitude control. The main workflow is a training or playback invocation selecting a task, which creates a task-specific simulation environment; the RL policy supplies actions and receives observations and rewards as the simulated Olympus robot evolves. Task environments also handle initialization, curricula, and task state where applicable. The README documents Earth, Mars, and zero-gravity variants plus policy playback; paths and call links for some of these capabilities are only supported at directory level because their implementation was not sampled.