Robot Learning

Temporal Abstraction

Temporal abstraction is the structuring of sequential decision-making at multiple time scales, so that a high-level policy selects extended behaviors, such as options, skills, or subgoals, while low-level policies handle fine-grained control within each behavior. The options framework of Sutton, Precup, and Singh formalized this in reinforcement learning, and hierarchical policies, skill discovery, and action chunking are all instances. Abstraction shortens effective horizons and simplifies credit assignment.

Why it matters for physical AI

Long-horizon household and industrial tasks are far easier to learn and plan over when decomposed into reusable skills, and modern systems pair vision-language models issuing abstract subgoals with low-level policies executing them.