DOMINO dataset and PUMA architecture enable better dynamic robotic manipulation by incorporating motion history, delivering 6.3% higher success rates than prior VLA models.
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6 Pith papers cite this work. Polarity classification is still indexing.
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A1 is a transparent VLA framework achieving state-of-the-art robot manipulation success with up to 72% lower latency via adaptive layer truncation and inter-layer flow matching.
LingBot-VLA is a VLA foundation model trained on massive real robot data that shows superior generalization across tasks and platforms with fast training throughput.
VLA-REPLICA is a low-cost and reproducible real-world benchmark for evaluating VLA models in robotic manipulation tasks.
ProcVLM learns procedure-grounded dense progress rewards for robotic manipulation via a reasoning-before-estimation VLM trained on a 60M-frame synthesized corpus from 30 embodied datasets.
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
citing papers explorer
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Towards Generalizable Robotic Manipulation in Dynamic Environments
DOMINO dataset and PUMA architecture enable better dynamic robotic manipulation by incorporating motion history, delivering 6.3% higher success rates than prior VLA models.
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A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model
A1 is a transparent VLA framework achieving state-of-the-art robot manipulation success with up to 72% lower latency via adaptive layer truncation and inter-layer flow matching.
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A Pragmatic VLA Foundation Model
LingBot-VLA is a VLA foundation model trained on massive real robot data that shows superior generalization across tasks and platforms with fast training throughput.
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VLA-REPLICA: A Low-Cost, Reproducible Benchmark for Real-World Evaluation of Vision-Language-Action Models
VLA-REPLICA is a low-cost and reproducible real-world benchmark for evaluating VLA models in robotic manipulation tasks.
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ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation
ProcVLM learns procedure-grounded dense progress rewards for robotic manipulation via a reasoning-before-estimation VLM trained on a 60M-frame synthesized corpus from 30 embodied datasets.
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World Action Models: The Next Frontier in Embodied AI
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.