MetaFine reconstructs benchmarks into diagnostic scenarios to evaluate vision-language-action models on fine-grained manipulation, exposing dimension-specific failures and identifying the visual encoder as a key bottleneck.
IEEE Robotics and Automation Letters7(3), 7327–7334 (2022)
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AnySlot decouples language grounding from low-level control by inserting an explicit visual goal image, yielding better zero-shot performance on precise slot placement tasks than flat VLA policies.
DC-QFA trains one supernet over architectures and bit-widths, then runs a fast per-device search plus multi-step distillation to deliver 2-3x faster robotic policies across hardware with negligible success-rate drop.
citing papers explorer
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Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation
MetaFine reconstructs benchmarks into diagnostic scenarios to evaluate vision-language-action models on fine-grained manipulation, exposing dimension-specific failures and identifying the visual encoder as a key bottleneck.
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AnySlot: Goal-Conditioned Vision-Language-Action Policies for Zero-Shot Slot-Level Placement
AnySlot decouples language grounding from low-level control by inserting an explicit visual goal image, yielding better zero-shot performance on precise slot placement tasks than flat VLA policies.
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Device-Conditioned Neural Architecture Search for Efficient Robotic Manipulation
DC-QFA trains one supernet over architectures and bit-widths, then runs a fast per-device search plus multi-step distillation to deliver 2-3x faster robotic policies across hardware with negligible success-rate drop.