RoboMIND is a large-scale multi-embodiment teleoperation dataset for robot manipulation containing 107k trajectories across four robots, with failure annotations and a digital twin simulator.
Generation and comprehension of unambiguous object descriptions
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A pipeline of chain-of-thought data synthesis, LoRA-based supervised fine-tuning, rejection sampling, and rule-based reinforcement learning raises multi-image grounding accuracy by 9.04% on MIG-Bench and 4.41% on average across seven other benchmarks.
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RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation
RoboMIND is a large-scale multi-embodiment teleoperation dataset for robot manipulation containing 107k trajectories across four robots, with failure annotations and a digital twin simulator.
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Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning
A pipeline of chain-of-thought data synthesis, LoRA-based supervised fine-tuning, rejection sampling, and rule-based reinforcement learning raises multi-image grounding accuracy by 9.04% on MIG-Bench and 4.41% on average across seven other benchmarks.