Coding Agents for Generalized Task and Motion Planning Problems
Quick summary
arXiv:2609.30233v1 Announce Type: cross Abstract: Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints. Generalized TAMP addresses this difficulty by exploiting regularities across problem instances to reduce planning effort on new instances. However, existing methods require substantial TAMP-specific engineering. We investigate whether coding agents can automate this process by synthesizing programs that generalize across instances. Given
Key takeaways
- arXiv:2609.30233v1 Announce Type: cross Abstract: Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints.
- Generalized TAMP addresses this difficulty by exploiting regularities across problem instances to reduce planning effort on new instances.
- However, existing methods require substantial TAMP-specific engineering.
Why it matters
The importance of “Coding Agents for Generalized Task and Motion Planning Problems” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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