Although large vision-language-action (VLA) models pretrained on extensive robot datasets offer promising generalist policies for robotic learning, they still struggle with spatial-temporal dynamics in interactive robotics, making them less effective in handling complex tasks, such as manipulation. In this work, we introduce visual trace prompting, a simple yet effective approach to facilitate VLA models' spatial-temporal awareness for action prediction by encoding state-action trajectories visually. We develop a new TraceVLA model by finetuning OpenVLA on our own collected dataset of 150K robot manipulation trajectories using visual trace prompting. Evaluations of TraceVLA across 137 configurations in SimplerEnv and 4 tasks on a physical WidowX robot demonstrate state-of-the-art performance, outperforming OpenVLA by 10% on SimplerEnv and 3.5x on real-robot tasks and exhibiting robust generalization across diverse embodiments and scenarios. To further validate the effectiveness and generality of our method, we present a compact VLA model based on 4B Phi-3-Vision, pretrained on the Open-X-Embodiment and finetuned on our dataset, rivals the 7B OpenVLA baseline while significantly improving inference efficiency.

Recently, instruction-tuned Large Multimodal Models (LMMs), such as InstructBLIP [1], Instruct-GPT [2], LLaVA [3, 4], PALM [5] and others have demonstrated state-of-the-art performance on a variety of vision-and-language tasks. However, existing LMMs for robotics [6, 7, 8, 9] do not always demonstrate the same success and consistency across various embodied settings. This may result from the unique challenges encountered in robotics, such as the variability of real-world environments, the differences between robots, and the need to control actions reliably. Since LMMs have been proven to be successful in part due to multimodal instruction tuning, it is natural to leverage this technique in a robotics setting as well. Here, we propose a vision-action instruction tuning method that can bridge the gap between a language model's fundamental pre-training objective--next-word prediction--and the goal of enabling the model to handle various robotics settings. In this work, we introduce our Large LAnguage model for Robotic Vision and Action (LLARVA), an open-source instruction-tuned LMM for robotic applications that can generalize efficiently across various environments and robotic configurations. Our key idea is the formulation of a novel instruction prompt that encapsulates robot type, task, scene configuration, and control regime in a natural language prefix amenable to contemporary LMMs.