Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations

• URL: http://arxiv.org/abs/2511.22697v1
• Date: Thu, 27 Nov 2025 18:50:21 GMT
• Title: Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations
• Authors: Chancharik Mitra, Yusen Luo, Raj Saravanan, Dantong Niu, Anirudh Pai, Jesse Thomason, Trevor Darrell, Abrar Anwar, Deva Ramanan, Roei Herzig,
• Abstract summary: Vision-Language Action (VLAs) models promise to extend the remarkable success of vision-language models (VLMs) to robotics.<n>Existing fine-tuning methods lack specificity, adapting the same set of parameters regardless of a task's visual, linguistic, and physical characteristics.<n>Inspired by functional specificity in neuroscience, we hypothesize that it is more effective to finetune sparse model representations specific to a given task.
• Score: 76.79742393097358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Action (VLAs) models promise to extend the remarkable success of vision-language models (VLMs) to robotics. Yet, unlike VLMs in the vision-language domain, VLAs for robotics require finetuning to contend with varying physical factors like robot embodiment, environment characteristics, and spatial relationships of each task. Existing fine-tuning methods lack specificity, adapting the same set of parameters regardless of a task's visual, linguistic, and physical characteristics. Inspired by functional specificity in neuroscience, we hypothesize that it is more effective to finetune sparse model representations specific to a given task. In this work, we introduce Robotic Steering, a finetuning approach grounded in mechanistic interpretability that leverages few-shot demonstrations to identify and selectively finetune task-specific attention heads aligned with the physical, visual, and linguistic requirements of robotic tasks. Through comprehensive on-robot evaluations with a Franka Emika robot arm, we demonstrate that Robotic Steering outperforms LoRA while achieving superior robustness under task variation, reduced computational cost, and enhanced interpretability for adapting VLAs to diverse robotic tasks.

Related papers

• Vision-Language-Policy Model for Dynamic Robot Task Planning [8.427578025752219]
  Gap between natural language commands and autonomous execution remains an open challenge for robotics.<n>Traditional robotic task-planning approaches often struggle to bridge low-level execution with high-level task reasoning.<n>We propose a novel language model-based framework for dynamic robot task planning.
  arXiv  Detail & Related papers  (2025-12-22T09:12:48Z)
• MiVLA: Towards Generalizable Vision-Language-Action Model with Human-Robot Mutual Imitation Pre-training [102.850162490626]
  We propose MiVLA, a vision-language-action model empowered by human-robot mutual imitation pre-training.<n>We show that MiVLA achieves strong improved generalization capability, outperforming state-of-the-art VLAs.
  arXiv  Detail & Related papers  (2025-12-17T12:59:41Z)
• ROSA: Harnessing Robot States for Vision-Language and Action Alignment [24.426285156386715]
  Vision-Language Models (VLMs) have made significant advance in end-to-end robotic control.<n>We propose a novel training paradigm, ROSA, which leverages robot state estimation to improve alignment between vision-language and action spaces.
  arXiv  Detail & Related papers  (2025-06-16T16:34:20Z)
• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [68.36528819227641]
  This paper systematically evaluates the robustness of Vision-Language-Action (VLA) models.<n>We introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory.<n>We design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs [0.0]
  Wonderful Team is a framework for executing high-level robotic planning in a zero-shot regime.<n>We show that Wonderful Team's performance on real-world semantic and physical planning tasks often exceeds methods that rely on separate vision systems.
  arXiv  Detail & Related papers  (2024-07-26T21:18:57Z)
• LLaRA: Supercharging Robot Learning Data for Vision-Language Policy [56.505551117094534]
  We introduce LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as visuo-textual conversations.<n>First, we present an automated pipeline to generate conversation-style instruction tuning data for robots from existing behavior cloning datasets.<n>We show that a VLM finetuned with a limited amount of such datasets can produce meaningful action decisions for robotic control.
  arXiv  Detail & Related papers  (2024-06-28T17:59:12Z)
• Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation Tasks [0.0]
  In this work, we focus on unsupervised vision-language--action mapping in the area of robotic manipulation.<n>We propose a model-invariant training alternative that improves the models' performance in a simulator by up to 55%.<n>Our work thus also sheds light on the potential benefits and limitations of using the current multimodal VAEs for unsupervised learning of robotic motion trajectories.
  arXiv  Detail & Related papers  (2024-04-02T13:25:16Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• SEAL: Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions [5.522839151632667]
  We extend the semantic frame representation for robot manipulation actions and introduce the problem of Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions (SEAL) as a graphical model. For the SEAL problem, we describe our nonparametric Semantic Frame Mapping (SeFM) algorithm for maintaining belief over a finite set of semantic frames as the locations of actions afforded to the robot.
  arXiv  Detail & Related papers  (2023-03-24T15:25:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.