PPF paper improves Digit locomotion with model-assumption-based regularization
Evidence notes
- PPF combines controller imitation, reinforcement-learning fine-tuning, and model-assumption-based regularization for humanoid locomotion.
- The paper reports hardware experiments on full-size Digit, including 1.5 m/s walking and robust locomotion on slippery, sloped, uneven, and sandy terrain.
Company context
US robotics company focused on humanoid systems for logistics and warehouse automation. Digit is designed for real-world material handling tasks in structured environments.