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Found 3 papers, 1 papers with code
Accelerating Integrated Task and Motion Planning with Neural Feasibility Checking
Task and Motion Planning (TAMP) provides a hierarchical framework to handle the sequential nature of manipulation tasks by interleaving a symbolic task planner that generates a possible action sequence, with a motion planner that checks the kinematic feasibility in the geometric world, generating robot trajectories if several constraints are satisfied, e. g., a collision-free trajectory from one state to another.
Extended Tree Search for Robot Task and Motion Planning
Moreover, we effectively combine this skeleton space with the resultant motion variable spaces into a single extended decision space.
Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems
We present a new family of deep neural network-based dynamic systems.
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