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The paper introduces dVLA, a diffusion-based Vision-Language-Action model that unifies visual perception, language reasoning, and robotic control under a single diffusion objective. dVLA incorporates a multimodal chain-of-thought to improve cross-modal reasoning and generalization. The model achieves state-of-the-art performance on the LIBERO benchmark (96.4% success rate) and demonstrates robust real-world performance on a Franka robot, including a challenging bin-picking task.
Diffusion models can now achieve state-of-the-art performance in vision-language-action robotics tasks, rivaling and surpassing discrete and continuous action policies.
Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics. We introduce dVLA, a diffusion-based VLA that leverages a multimodal chain-of-thought to unify visual perception, language reasoning, and robotic control in a single system. dVLA jointly optimizes perception, language understanding, and action under a single diffusion objective, enabling stronger cross-modal reasoning and better generalization to novel instructions and objects. For practical deployment, we mitigate inference latency by incorporating two acceleration strategies, a prefix attention mask and KV caching, yielding up to around times speedup at test-time inference. We evaluate dVLA in both simulation and the real world: on the LIBERO benchmark, it achieves state-of-the-art performance with a 96.4% average success rate, consistently surpassing both discrete and continuous action policies; on a real Franka robot, it succeeds across a diverse task suite, including a challenging bin-picking task that requires multi-step planning, demonstrating robust real-world performance. Together, these results underscore the promise of unified diffusion frameworks for practical, high-performance VLA robotics.
Tsinghua AI