arXiv AI
Akule: Fast and Scalable Multi-Robot Motion Planning via Sparse Interaction Diffusion
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Training-Free Diffusion Planning with Analytical Local Scores
The paper presents a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. By reconstructing trajectory scores through local interactions between neighboring waypoints and nearby constraints, the method decomposes the denoising process while preserving the optimization structure of classical trajectory methods. Experiments demonstrate that this approach generates smooth, feasible trajectories for large multi-agent tasks in complex environments quickly, outperforming learning-based and optimization baselines without requiring training data.
arXiv Machine Learning
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Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation
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arXiv Machine Learning
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REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
arXiv:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.
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Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
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arXiv AI
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Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning
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arXiv Machine Learning
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Immiscible Diffusion Policy: Preserving Multimodal Robot Actions through Label-Free Noise Assignment
The paper introduces Immiscible Diffusion Policy, a label‑free training add‑on that prevents diffusion policies from collapsing to a single action modality. By assigning action‑noise pairs, it preserves distinct noise‑to‑action routes without altering the policy architecture or inference. Experiments on five simulated and two real‑world humanoid manipulation tasks show significant improvements in maintaining multimodal action distributions, increasing non‑dominant modality proportions by up to 14.6× and recovering missing modalities in four‑modality tasks.