AnchorRefine factorizes VLA action generation into a trajectory anchor for coarse planning and residual refinement for local corrections, improving success rates by up to 7.8% in simulation and 18% on real robots across LIBERO, CALVIN, and physical tasks.
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A single transformer model trained offline on expert trajectories from three distinct MARL environments achieves competitive performance against specialized baselines without per-task tuning.
citing papers explorer
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AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models
AnchorRefine factorizes VLA action generation into a trajectory anchor for coarse planning and residual refinement for local corrections, improving success rates by up to 7.8% in simulation and 18% on real robots across LIBERO, CALVIN, and physical tasks.
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MARL-GPT: Foundation Model for Multi-Agent Reinforcement Learning
A single transformer model trained offline on expert trajectories from three distinct MARL environments achieves competitive performance against specialized baselines without per-task tuning.