CODA augments offline multi-agent RL with on-policy diffusion trajectories that evolve with the joint policy to enable coordination.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Late fusion of asynchronous vehicle predictions improves trajectory success rate (TSR_0.5) by 1.22-1.69% on real-world V2V4Real data compared to single-vehicle forecasting.
A visibility-aware mobile grasping system with iterative whole-body planning and behavior-tree subgoal generation achieves 68.8% success in unknown static and 58% in dynamic environments, outperforming a baseline by 22.8% and 18%.
citing papers explorer
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CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
CODA augments offline multi-agent RL with on-policy diffusion trajectories that evolve with the joint policy to enable coordination.
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Collaborative Trajectory Prediction via Late Fusion
Late fusion of asynchronous vehicle predictions improves trajectory success rate (TSR_0.5) by 1.22-1.69% on real-world V2V4Real data compared to single-vehicle forecasting.
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Visibility-Aware Mobile Grasping in Dynamic Environments
A visibility-aware mobile grasping system with iterative whole-body planning and behavior-tree subgoal generation achieves 68.8% success in unknown static and 58% in dynamic environments, outperforming a baseline by 22.8% and 18%.