Real-World Robot Applications of Foundation Models: A Review

• URL: http://arxiv.org/abs/2402.05741v2
• Date: Wed, 23 Oct 2024 03:39:00 GMT
• Title: Real-World Robot Applications of Foundation Models: A Review
• Authors: Kento Kawaharazuka, Tatsuya Matsushima, Andrew Gambardella, Jiaxian Guo, Chris Paxton, Andy Zeng,
• Abstract summary: Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), facilitate flexible application across different tasks and modalities. This paper provides an overview of the practical application of foundation models in real-world robotics.
• Score: 25.53250085363019
• License:
• Abstract: Recent developments in foundation models, like Large Language Models (LLMs) and Vision-Language Models (VLMs), trained on extensive data, facilitate flexible application across different tasks and modalities. Their impact spans various fields, including healthcare, education, and robotics. This paper provides an overview of the practical application of foundation models in real-world robotics, with a primary emphasis on the replacement of specific components within existing robot systems. The summary encompasses the perspective of input-output relationships in foundation models, as well as their role in perception, motion planning, and control within the field of robotics. This paper concludes with a discussion of future challenges and implications for practical robot applications.

Related papers

• $π_0$: 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)
• Differentiable Robot Rendering [45.23538293501457]
  We introduce differentiable robot rendering, a method allowing the visual appearance of a robot body to be directly differentiable with respect to its control parameters. We demonstrate its capability and usage in applications including reconstruction of robot poses from images and controlling robots through vision language models.
  arXiv  Detail & Related papers  (2024-10-17T17:59:02Z)
• Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments [26.66666135624716]
  We present Robot Utility Models (RUMs), a framework for training and deploying zero-shot robot policies. RUMs can generalize to new environments without any finetuning. We train five utility models for opening cabinet doors, opening drawers, picking up napkins, picking up paper bags, and reorienting fallen objects.
  arXiv  Detail & Related papers  (2024-09-09T17:59:50Z)
• A Survey on Robotics with Foundation Models: toward Embodied AI [30.999414445286757]
  Recent advances in computer vision, natural language processing, and multi-modality learning have shown that the foundation models have superhuman capabilities for specific tasks. This survey aims to provide a comprehensive and up-to-date overview of foundation models in robotics, focusing on autonomous manipulation and encompassing high-level planning and low-level control.
  arXiv  Detail & Related papers  (2024-02-04T07:55:01Z)
• AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents [109.3804962220498]
  AutoRT is a system to scale up the deployment of operational robots in completely unseen scenarios with minimal human supervision. We demonstrate AutoRT proposing instructions to over 20 robots across multiple buildings and collecting 77k real robot episodes via both teleoperation and autonomous robot policies. We experimentally show that such "in-the-wild" data collected by AutoRT is significantly more diverse, and that AutoRT's use of LLMs allows for instruction following data collection robots that can align to human preferences.
  arXiv  Detail & Related papers  (2024-01-23T18:45:54Z)
• Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis [82.59451639072073]
  General-purpose robots operate seamlessly in any environment, with any object, and utilize various skills to complete diverse tasks. As a community, we have been constraining most robotic systems by designing them for specific tasks, training them on specific datasets, and deploying them within specific environments. Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models, we devote this survey to exploring how foundation models can be applied to general-purpose robotics.
  arXiv  Detail & Related papers  (2023-12-14T10:02:55Z)
• Transferring Foundation Models for Generalizable Robotic Manipulation [82.12754319808197]
  We propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models. Our approach can effectively and robustly perceive object pose and enable sample-efficient generalization learning. Demos can be found in our submitted video, and more comprehensive ones can be found in link1 or link2.
  arXiv  Detail & Related papers  (2023-06-09T07:22:12Z)
• Foundation Models for Decision Making: Problems, Methods, and Opportunities [124.79381732197649]
  Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. New paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems.
  arXiv  Detail & Related papers  (2023-03-07T18:44:07Z)
• RT-1: Robotics Transformer for Real-World Control at Scale [98.09428483862165]
  We present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks.
  arXiv  Detail & Related papers  (2022-12-13T18:55:15Z)

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.