CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
On the map: Vision-language-action models → Generalist VLA models and benchmarks
Abstract (arXiv)
The advancement of large Vision-Language-Action (VLA) models has significantly improved robotic manipulation in terms of language-guided task execution and generalization to unseen scenarios. While existing VLAs adapted from pretrained large Vision-Language-Models (VLM) have demonstrated promising generalizability, their task performance is still unsatisfactory as indicated by the low tasks success rates in different environments. In this paper, we present a new advanced VLA architecture derived from VLM. Unlike previous works that directly repurpose VLM for action prediction by simple action quantization, we propose a omponentized VLA architecture that has a specialized action module conditioned on VLM output. We systematically study the design of the action module and demonstrates the strong performance enhancement with diffusion action transformers for action sequence modeling, as well as their favorable scaling behaviors. We also conduct comprehensive experiments and ablation studies to evaluate the efficacy of our models with varied designs. The evaluation on 5 robot embodiments in simulation and real work shows that our model not only significantly surpasses existing VLAs in task performance and but also exhibits remarkable adaptation to new robots and generalization to unseen objects and backgrounds. It exceeds the average success rates of OpenVLA which has similar model size (7B) with ours by over 35% in simulated evaluation and 55% in real robot experiments. It also outperforms the large RT-2-X model (55B) by 18% absolute success rates in simulation. Code and models can be found on our project page (https://cogact.github.io/).
Key facts. Cited by 411 papers in the map (data through 7 Oct 2026). Placed in Generalist VLA models and benchmarks, one of 13 directions in Vision-language-action models. Builds on 58 of its 67 references that are in the map. First posted on arXiv 29 Nov 2024.
Builds on
58 of 67 references.
Closest ancestors · 3
Same direction, newest first
- 2024TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation19 Sep
- 2024OpenVLA: An Open-Source Vision-Language-Action Model13 Jun
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control28 Jul
Other references · 55
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- 2021Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasetscited by 248
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- 2022Interactive Language: Talking to Robots in Real Timecited by 217
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50 most cited of 55 All 55 (JSON)
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Citing papers per year: 2024: 5; 2025: 170; 2026: 236.
411 papers.
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