ST-BiBench reveals a coordination paradox in which MLLMs show strong high-level strategic reasoning yet fail at fine-grained 16-dimensional bimanual action synthesis and multi-stream fusion.
Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0
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ICR-Drive reveals substantial performance drops in end-to-end language-driven driving models when instructions are paraphrased, made ambiguous, noised, or misleading.
ARM trains reward models on Progressive/Regressive/Stagnant labels to enable adaptive reweighting in offline RL, reaching 99.4% success on towel-folding with minimal human intervention.
citing papers explorer
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ST-BiBench: Benchmarking Multi-Stream Multimodal Coordination in Bimanual Embodied Tasks for MLLMs
ST-BiBench reveals a coordination paradox in which MLLMs show strong high-level strategic reasoning yet fail at fine-grained 16-dimensional bimanual action synthesis and multi-stream fusion.
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ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving
ICR-Drive reveals substantial performance drops in end-to-end language-driven driving models when instructions are paraphrased, made ambiguous, noised, or misleading.
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ARM: Advantage Reward Modeling for Long-Horizon Manipulation
ARM trains reward models on Progressive/Regressive/Stagnant labels to enable adaptive reweighting in offline RL, reaching 99.4% success on towel-folding with minimal human intervention.