Filtered to vision-language-action models · clear filter
This paper introduces TurboVLA, a new vision-language-action model that reduces computation and memory overhead by directly exchanging information between visual observations and language instructions, allowing for faster and more efficient robotic manipulation. Practitioners might care about this approach for building more efficient and effective VLA models.
This paper presents GigaBrain-0.7, a new embodied foundation model that achieves strong generalization across diverse robot embodiments and tasks, by improving the architecture and scaling it to large amounts of data. Practitioners might care about this research if they're working on developing generalist robots that can adapt to new tasks and environments.
This paper introduces a vision-language-action model that can perform mobile manipulation tasks in unseen environments with minimal training data, and how it can be scaled up to achieve better performance. Practitioners might care about this model for building robots that can adapt to new tasks with minimal fine-tuning.
This paper proposes a new approach to training Vision-Language-Action models by using a pre-trained backbone that captures generalizable visual-action knowledge from a large, diverse dataset of robot trajectories. This allows the model to perform well on new, unseen tasks without requiring a large amount of task-specific data.