Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory Planning
Non-prehensile manipulation with mobile robots has primarily targeted placement in coarse goal regions.
ProofPaper ↗
Key points
- Precision placement has been studied with manipulators on benchmarks such as Push-T, where an omnidirectional end effector can readily re-approach an object from any side.
- We formalize multi-object pushing with a mobile robot as a multi-target assignment problem at tight tolerances.
- We propose a diffusion planner with a causal factorization: each robot denoising step is followed by an object step conditioned on the current clean robot estimate.
- We also design a set of pushing-specific costs for mobile robots, used for both guidance and best-of-$N$ sampling at inference.
Sources (1)
- [1]Non-Prehensile Multi-Object Placement with Mobile Robots via Diffusion-Based Trajectory PlanningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 08:09 PM
Non-prehensile manipulation with mobile robots has primarily targeted placement in coarse goal regions.
Precision placement has been studied with manipulators on benchmarks such as Push-T, where an omnidirectional end effector can readily re-approach an object from any side.
Extractive summary: sentences quoted from the sources.
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