Robot Learning

Conditional Imitation Learning

Conditional imitation learning is imitation learning in which the policy receives an additional conditioning input, such as a navigation command, goal image, or language instruction, that disambiguates which of several valid expert behaviors to reproduce. Introduced prominently for autonomous driving by Codevilla et al. (2018), where turn commands resolved intersection ambiguity, the idea generalizes to goal- and language-conditioned manipulation policies. Conditioning resolves the multimodality that plain behavior cloning averages away.

Why it matters for physical AI

Generalist robot policies are inherently conditional, executing whichever task the instruction specifies, so conditioning architecture and grounding quality largely determine how well one model serves many tasks.