LLaRA: Supercharging Robot Learning Data for Vision-Language Policy

• URL: http://arxiv.org/abs/2406.20095v2
• Date: Fri, 04 Oct 2024 03:28:30 GMT
• Title: LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
• Authors: Xiang Li, Cristina Mata, Jongwoo Park, Kumara Kahatapitiya, Yoo Sung Jang, Jinghuan Shang, Kanchana Ranasinghe, Ryan Burgert, Mu Cai, Yong Jae Lee, Michael S. Ryoo,
• Abstract summary: Vision Language Models (VLMs) can process state information as visual-textual prompts and respond with policy decisions in text. We propose LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as conversations.
• Score: 56.505551117094534
• License:
• Abstract: LLMs with visual inputs, i.e., Vision Language Models (VLMs), have the capacity to process state information as visual-textual prompts and respond with policy decisions in text. We propose LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as conversations and provides improved action outputs when trained with auxiliary data that complements policy learning. We first introduce an automated pipeline to generate conversation-style instruction tuning data from existing behavior cloning data. Then we enrich the dataset in a self-supervised fashion by formulating six auxiliary tasks. A VLM finetuned with the resulting collection of datasets can generate meaningful robot action policy decisions. Our experiments across multiple simulated and real-world environments demonstrate the state-of-the-art performance of the proposed LLaRA framework. The code, datasets, and pretrained models are available at https://github.com/LostXine/LLaRA.

Related papers

• KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data [45.25288643161976]
  We propose Keypoint Affordance Learning from Imagined Environments (KALIE) for robotic control in a scalable manner. Instead of directly producing motor commands, KALIE controls the robot by predicting point-based affordance representations. We demonstrate that KALIE can learn to robustly solve new manipulation tasks with unseen objects given only 50 example data points.
  arXiv  Detail & Related papers  (2024-09-21T08:45:16Z)
• CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving [1.727597257312416]
  CoVLA (Comprehensive Vision-Language-Action) dataset comprises real-world driving videos spanning more than 80 hours. This dataset establishes a framework for robust, interpretable, and data-driven autonomous driving systems.
  arXiv  Detail & Related papers  (2024-08-19T09:53:49Z)
• LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning [50.99807031490589]
  We introduce LLARVA, a model trained with a novel instruction tuning method to unify a range of robotic learning tasks, scenarios, and environments. We generate 8.5M image-visual trace pairs from the Open X-Embodiment dataset in order to pre-train our model. Experiments yield strong performance, demonstrating that LLARVA performs well compared to several contemporary baselines.
  arXiv  Detail & Related papers  (2024-06-17T17:55:29Z)
• PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning [2.7387720378113554]
  Preference-based reinforcement learning (RL) has emerged as a new field in robot learning. We use the zero-shot capabilities of a large language model (LLM) to reason from the text provided by humans. In both a simulated scenario and a user study, we reveal the effectiveness of our work by analyzing the feedback and its implications.
  arXiv  Detail & Related papers  (2024-02-23T16:30:05Z)
• PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs [140.14239499047977]
  Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. We propose a novel visual prompting approach for VLMs that we call Prompting with Iterative Visual Optimization (PIVOT) We find, perhaps surprisingly, that our approach enables zero-shot control of robotic systems without any robot training data, navigation in a variety of environments, and other capabilities.
  arXiv  Detail & Related papers  (2024-02-12T18:33:47Z)
• 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)
• Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models [70.82705830137708]
  We introduce Data-driven Instruction Augmentation for Language-conditioned control (DIAL) We utilize semi-language labels leveraging the semantic understanding of CLIP to propagate knowledge onto large datasets of unlabelled demonstration data. DIAL enables imitation learning policies to acquire new capabilities and generalize to 60 novel instructions unseen in the original dataset.
  arXiv  Detail & Related papers  (2022-11-21T18:56:00Z)
• Modular Framework for Visuomotor Language Grounding [57.93906820466519]
  Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research. We propose the structuring of language, acting, and visual tasks into separate modules that can be trained independently.
  arXiv  Detail & Related papers  (2021-09-05T20:11:53Z)

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.