Robot Learning

Multi-Task Learning

Multi-task learning is the training of a single model on multiple tasks simultaneously so that shared structure improves data efficiency and generalization relative to per-task models. In robot learning, multi-task policies condition on task identity through one-hot vectors, goal images, or language instructions, as in RT-1 and MT-Opt. Challenges include gradient interference between tasks, data imbalance, and negative transfer, addressed by loss weighting and gradient surgery methods.

Why it matters for physical AI

Generalist robot policies are, by construction, extreme multi-task learners; positive transfer across thousands of skills is the core bet behind training one foundation model instead of many specialists.