A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper introduces Fast, an efficient action tokenization method for vision-language-action models, aiming to improve the speed and practicality of generalist robot policies.
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Introduces RIPT-VLA, a reinforcement-learning-based interactive post-training paradigm for fine-tuning pretrained Vision-Language-Action models.
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This paper introduces Action Chain-of-Thought (ACoT) for Vision-Language-Action models, decomposing action generation into a step-by-step reasoning process to improve manipulation performance and interpretability.
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Villa-x enhances latent action modeling in Vision-Language-Action models to improve robot manipulation policy learning and generalization.
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This paper describes standard recipes for building and training vision-language-action models (VLAs) that adapt VLMs to output robot actions, enabling fast training, fast inference, and better generalization.
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This paper presents the first comprehensive survey of Vision-Language-Action models for Autonomous Driving (VLA4AD), bridging the gap between classic AV reviews and the emerging VLA paradigm.
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This paper introduces RT-2, a vision-language-action model that transfers web knowledge to robotic control, enabling improved generalization and new capabilities.
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This paper investigates fine-tuning strategies for vision-language-action models, focusing on optimizing inference speed and task success rates.
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This survey provides a comprehensive review of vision-language-action models from the perspective of action tokenization, categorizing methods and discussing challenges and future directions.