Manipulation

Task and Motion Planning (TAMP)

Task and motion planning (TAMP) is the integrated problem of choosing a discrete sequence of symbolic actions, such as pick, place, or open, together with the continuous motions, grasps, and placements that realize each action. Because the feasibility of a symbolic plan depends on geometric details like reachability and collision-free paths, TAMP solvers interleave symbolic search with continuous constraint satisfaction, as in systems like PDDLStream. It remains the standard formulation for long-horizon manipulation with strong correctness guarantees.

Why it matters for physical AI

Long-horizon tasks still challenge end-to-end learned policies, and hybrid systems that pair foundation-model task reasoning with TAMP-style geometric verification are a leading approach to reliable multi-step manipulation in deployment.