The paper introduces Coordinated Multi-Agent Diffusion Steering (CMDS), a framework that reuses independently trained diffusion models as generative primitives and learns a control policy to coordinate their reverse processes. By framing coordination as a stochastic optimal control problem, CMDS balances an assembly-level reward with adherence to pretrained dynamics, enabling the learned control to generalise across new tasks. Experiments demonstrate CMDS’s ability to recover target distributions, satisfy spatial constraints, and separate sources from degraded mixtures in applications such as maze navigation, robot planning, and text-conditioned human motion.
arXiv:2606. 08657v1 Announce Type: cross Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process.
arXiv:2602. 18291v2 Announce Type: replace Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination.
arXiv:2606. 00336v1 Announce Type: new Abstract: We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior manifold.
arXiv:2606. 01151v1 Announce Type: new Abstract: Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift.
arXiv:2606. 19656v1 Announce Type: cross Abstract: A natural recipe for intelligent robotic decision-making is initializing from pretrained generative control policies, which have summarized offline experience, and adapting them to self-collected online experience.
The paper reviews how reinforcement learning (RL) can refine diffusion models (DMs) after pretraining, addressing limitations in reasoning, exploration, and alignment. It surveys RL-driven DMs across language, multimodal, robotic, and scientific domains, focusing on trajectory optimization and reward engineering. Experiments highlight the benefits of various RL post‑training strategies and identify key bottlenecks—reward fragility, exploration‑exploitation trade‑offs, and training instability—while proposing a roadmap for future research and a public repository for updates.
arXiv:2606. 02337v1 Announce Type: new Abstract: Constrained Multi-agent reinforcement learning (CMARL) faces two intertwined challenges: the joint action space grows exponentially with the number of agents, and additional requirements couple agents in ways that reward structure alone does not capture.
arXiv:2605.13775v2 Announce Type: replace-cross Abstract: The scalability of robotic manipulation is fundamentally bottlenecked by the scarcity of task-aligned physical interaction data. While vision...
arXiv:2512. 07212v3 Announce Type: replace Abstract: Imitation learning with diffusion models has advanced robotic control by capturing the multi-modal action distributions.
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.
The paper introduces Diffusion Skill Discovery (DSD), a method that employs a diffusion model to approximate the entropy gradient of policy-induced state distributions via score matching. This approach encourages the learning of a diverse repertoire of motor skills for high‑dimensional humanoid control, overcoming limitations of prior mutual‑information based methods that rely on indirect state entropy estimates. The discovered skills are effectively reused in hierarchical control and zero‑shot tasks, yielding more complex and agile behaviors than previous skill discovery techniques.
arXiv:2605. 12236v2 Announce Type: replace-cross Abstract: Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration.