We use reinforcement learning in NVIDIA Isaac Lab to train Boston Dynamics' Spot to move using only its hind legs. Despite several challenges, we deploy the policy in the real world and show how well the sim-to-real policy transfer works.
Military logistics planning under adversarial conditions is notoriously brittle. At Multi AI, we built a coordination platform integrating decentralized MCTS and generative AI to optimize theater-scale plans — delivering 38% more resources in field …
From voice command to synchronized flip: we show our early autonomous mission planning work that grew into Multi AI, including Tello drone choreography and a loyal wingman simulation for unmanned-manned teaming.
Standard path planners proved insufficient for dense multi-drone environments. We develop custom algorithms on top of EGO-Planner to enable robust obstacle avoidance in the Texas Robotics motion capture space, supporting advanced multi-agent …