Multi-task Learning

Multi-task learning trains a single policy on demonstrations from multiple distinct tasks simultaneously, with the expectation that shared representations learned across tasks improve performance on each individual task and enable generalization to new tasks. In robotics, this often means training on hundreds of tasks with varied objects, goals, and environments. The key challenge is balancing the gradient contributions of different tasks (gradient interference) and ensuring the policy can distinguish between tasks at inference time — typically via language conditioning or one-hot task identifiers. Multi-task policies are a prerequisite for general-purpose robotic assistants.