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Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
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Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the environment. Despite this promise, applying diffusion models to motion planning remains challenging due to their difficulty in enforcing critical constraints, such as collision avoidance and kinematic feasibility. These limitations become even more pronounced in Multi-Robot Motion Planning (MRMP), where multiple robots must coordinate in shared spaces. To address these challenges, this work proposes Simultaneous MRMP Diffusion (SMD), a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories. Additionally, the paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints. Experimental results show SMD consistently outperforms classical and other learning-based motion planners, achieving higher success rates and efficiency in complex multi-robot environments.
Forward citations
Cited by 5 Pith papers
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Motion Planning with Model-Based Diffusion via Constraint Optimization and Adaptive Scheduling
MD-COAS unifies inexact augmented-Lagrangian soft constraints with convex-feasible-set hard projection and adaptively schedules them during model-based diffusion, improving safe and successful planning in non-convex e...
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Pointwise constrained fine-tuning via sample-wise augmented Lagrangians and learned relaxations reduces tail constraint violations across safety, tool-calling, and re-ranking while preserving average task performance.
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Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization
Flow-Opt combines a flow-matching DiT model with a custom differentiable safety filter and learned initialization to enable fast centralized trajectory optimization for tens of robots.
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Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning
DGD uses MAPF plans to guide diffusion sampling inside convex regions and claims scalable multi-robot motion planning, but the cross-region collision assumption is not supported.
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Integration Matters: Rollout-Based Training for Constrained Diffusion Models
Rollout-based fine-tuning with a learned adaptive guidance scaling yields near-zero constraint violations while preserving sample fidelity in constrained diffusion models.
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