Task and Motion Planning
21 papers with code • 0 benchmarks • 0 datasets
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STRIPS Planning in Infinite Domains
We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints.
AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
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.
Active model learning and diverse action sampling for task and motion planning
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.
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
To complete the task, an embodied agent must plan a sequence of actions to change the state of a large number of objects in the face of realistic physical constraints.
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Bayesian optimization usually assumes that a Bayesian prior is given.
Learning to combine primitive skills: A step towards versatile robotic manipulation
rstrudel/rlbc
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Manipulation tasks such as preparing a meal or assembling furniture remain highly challenging for robotics and vision.
Learning compositional models of robot skills for task and motion planning
We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling.
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
tomsilver/camps
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A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent.
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
tomsilver/ploi
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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.
Learning Symbolic Operators for Task and Motion Planning
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.
