Task and Motion Planning
16 papers with code • 0 benchmarks • 0 datasets
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Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan.
Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world.
Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision.
A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent.
We conclude that learning to predict a sufficient set of objects for a planning problem is a simple, powerful, and general mechanism for planning in large instances.
We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system.
Moreover, we effectively combine this skeleton space with the resultant motion variable spaces into a single extended decision space.