Sensorimotor Policy

Sensorimotor policy learning focuses on enabling robots and autonomous systems to learn effective control strategies directly from sensory inputs, bypassing the need for explicit state estimation and planning. Current research emphasizes improving the efficiency and robustness of these policies, often employing imitation learning from expert demonstrations (e.g., from Model Predictive Controllers) or reinforcement learning techniques, sometimes augmented by data augmentation strategies like those using Neural Radiance Fields. Prominent approaches leverage deep neural networks to map sensory data (especially vision) to control actions, achieving impressive results in applications such as drone flight and autonomous driving. This field is crucial for advancing robotics and AI, enabling more agile, adaptable, and robust autonomous systems in complex real-world environments.