Masked Generative Motion Planning with Geometry-Guided Token Search
We introduce Masked Generative Motion Planning (MGMP), which extends the learned prior from efficient parallel generation to structural repair.
ProofPaper ↗
Key points
- Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refinement.
- A masked generative transformer generates discrete trajectory candidates in parallel, and Geometry-Guided Token Search (GGTS) uses scene geometry to target where to edit and which prior-supported alternatives to evaluate.
- This turns refinement into an efficient search over discrete motion alternatives, enabling route-level restructuring beyond local trajectory deformation.
- It further generalizes to unseen layouts, additional obstacles, unseen geometries, single- and dual-arm planning, and real-world Baxter tasks.
Sources (1)
- [1]Masked Generative Motion Planning with Geometry-Guided Token SearcharXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 03:19 PM
We introduce Masked Generative Motion Planning (MGMP), which extends the learned prior from efficient parallel generation to structural repair.
Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refinement.
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