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Found 7 papers, 1 papers with code
The Planner Optimization Problem: Formulations and Frameworks
Identifying internal parameters for planning is crucial to maximizing the performance of a planner.
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning
Most methods learn state representations by utilizing losses based on the reconstruction of the raw observations from a lower-dimensional latent space.
Learning Sampling Distributions Using Local 3D Workspace Decompositions for Motion Planning in High Dimensions
Earlier work has shown that reusing experience from prior motion planning problems can improve the efficiency of similar, future motion planning queries.
LTLf Synthesis on Probabilistic Systems
Linear Temporal Logic over finite traces (LTLf) has been used to express such properties, but no tools exist to solve policy synthesis for MDP behaviors given finite-trace properties.
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?
Deep Reinforcement Learning is a promising paradigm for robotic control which has been shown to be capable of learning policies for high-dimensional, continuous control of unmodeled systems.
Using Local Experiences for Global Motion Planning
In this work, we decompose the workspace into local primitives, memorizing local experiences by these primitives in the form of local samplers, and store them in a database.
Bounded Policy Synthesis for POMDPs with Safe-Reachability Objectives
In this work, we study POMDPs with safe-reachability objectives, which require that with a probability above some threshold, a goal state is eventually reached while keeping the probability of visiting unsafe states below some threshold.
