Conditioned Score-Based Models for Learning Collision-Free Trajectory GenerationDownload PDF

Published: 29 Nov 2022, Last Modified: 05 May 2023SBM 2022 PosterReaders: Everyone
Keywords: score-based models, motion planning, learning to plan
Abstract: Planning a motion in a cluttered environment is a recurring task autonomous agents need to solve. This paper presents a first attempt to learn generative models for collision-free trajectory generation based on conditioned score-based models. Given multiple navigation tasks, environment maps and collision-free trajectories pre-computed with a sample-based planner, using a signed distance function loss we learn a vision encoder of the map and use its embedding to learn a conditioned score-based model for trajectory generation. A novelty of our method is to integrate in a temporal U-net architecture conditioning variables such as the latent representation of the environment and task features, using a cross-attention mechanism. We validate our approach in a simulated 2D planar navigation toy task, where a robot needs to plan a path that avoids obstacles in a scene.
Student Paper: Yes
TL;DR: Encode motion planning generation with score-based models
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