Learning Task and Motion Plans from Real Demonstrations with Hybrid Flow Matching
Long-horizon mobile manipulation requires a task plan and the motion that executes it. Generative planners trained on demonstration produce both in one pass, requiring neither a symbolic domain nor search. To date, however, they have relied on thousands of scripted demonstrations of fixed-base arms and executed open loop. This paper presents a hybrid flow matching planner: a single network generates the symbolic plan with masked discrete flow matching and the motion trajectory with continuous flow matching. Unlike prior generative planners, we aim to learn from a much smaller set of demonstrations and to execute the plan in closed loop. Two properties of the data compensate for the small ...
| Authors | Zuleika Redondo Garcia, Andreu Matoses Gimenez, Javier Alonso-Mora |
|---|---|
| Primary category | cs.RO |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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