Robot Learning

Context-Conditioned Policy

A context-conditioned policy is a policy whose behavior is modulated by a context variable summarizing task, environment, or embodiment properties, such as a latent vector inferred from recent experience, a task embedding, or explicit system parameters. In meta-RL methods like PEARL, the context is inferred online from transitions, enabling rapid adaptation; in multi-task learning, contexts index skills. The policy shares parameters across contexts, amortizing learning.

Why it matters for physical AI

Fleet-scale deployment demands single models that adapt to varied robots, payloads, and sites; context conditioning provides the mechanism for such adaptation without per-site retraining.