search
HomeTechnology peripheralsAIReinforcement learning guru Sergey Levine's new work: Three large models teach robots to recognize their way

​The robot with a built-in large model has learned to follow language instructions to reach its destination without looking at a map. This achievement comes from the new work of reinforcement learning expert Sergey Levine.

Given a destination, how difficult is it to reach it smoothly without navigation tracks?

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

This task is also very challenging for humans with poor sense of direction. But in a recent study, several academics "taught" the robot using only three pre-trained models.

We all know that one of the core challenges of robot learning is to enable robots to perform a variety of tasks according to high-level human instructions. This requires robots that can understand human instructions and be equipped with a large number of different actions to carry out these instructions in the real world.

For instruction following tasks in navigation, previous work has mainly focused on learning from trajectories annotated with textual instructions. This may enable understanding of textual instructions, but the cost of data annotation has hindered widespread use of this technique. On the other hand, recent work has shown that self-supervised training of goal-conditioned policies can learn robust navigation. These methods are based on large, unlabeled datasets, with post hoc relabeling to train vision-based controllers. These methods are scalable, general, and robust, but often require the use of cumbersome location- or image-based target specification mechanisms.

In a latest paper, researchers from UC Berkeley, Google and other institutions aim to combine the advantages of these two methods to make a self-supervised system for robot navigation applicable to navigation data without any user annotations. , leveraging the ability of pre-trained models to execute natural language instructions. Researchers use these models to build an "interface" that communicates tasks to the robot. This system leverages the generalization capabilities of pre-trained language and vision-language models to enable robotic systems to accept complex high-level instructions.

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

  • Paper link: https://arxiv.org/pdf/2207.04429.pdf
  • Code link: https://github.com/blazejosinski/lm_nav

The researchers observed that it is possible to leverage off-the-shelf pre-trained models trained on large corpora of visual and language datasets ( These corpora are widely available and show zero-shot generalization capabilities) to create interfaces that enable specific instruction tracking. To achieve this, the researchers combined the advantages of vision and language robot-agnostic pre-trained models as well as pre-trained navigation models. Specifically, they used a visual navigation model (VNM:ViNG) to create a robot's visual output into a topological "mental map" of the environment. Given a free-form text instruction, a pre-trained large language model (LLM: GPT-3) is used to decode the instruction into a series of text-form feature points. Then, a visual language model (VLM: CLIP) is used to establish these text feature points in the topological map by inferring the joint likelihood of feature points and nodes. A new search algorithm is then used to maximize the probabilistic objective function and find the robot's instruction path, which is then executed by the VNM. The main contribution of the research is the navigation method under large-scale models (LM Nav), a specific instruction tracking system. It combines three large independent pre-trained models - a self-supervised robot control model that leverages visual observations and physical actions (VNM), a visual language model that places images within text but without a concrete implementation environment (VLM), and a large language model that parses and translates text but has no visual basis or embodied sense (LLM) to enable long-view instruction tracking in complex real-world environments. For the first time, researchers instantiated the idea of ​​combining pre-trained vision and language models with target-conditional controllers to derive actionable instruction paths in the target environment without any fine-tuning. Notably, all three models are trained on large-scale datasets, have self-supervised objective functions, and are used out-of-the-box without fine-tuning - training LM Nav does not require human annotation of robot navigation data.

Experiments show that LM Nav is able to successfully follow natural language instructions in a new environment while using fine-grained commands to remove path ambiguity during complex suburban navigation up to 100 meters.

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

​LM-Nav model overview

So, how do researchers use pre-trained image and language models to provide text interfaces for visual navigation models?

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

​1. Given a set of observations in the target environment, use the target conditional distance function, which is the visual navigation model (VNM) part, infer the connectivity between them, and build a topological map of the connectivity in the environment.

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

## 2. Large language model (LLM) is used to parse natural language instructions into a series of feature points, these Feature points can be used as intermediate sub-goals for navigation.

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

3. Visual-language model (VLM) is used to establish visual observations based on feature point phrases. The vision-language model infers a joint probability distribution over the feature point descriptions and images (forming the nodes in the graph above).

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

​4. Using the probability distribution of VLM and the graph connectivity inferred by VNM, adopts a novel search algorithm , retrieve an optimal instruction path in the environment, which (i) satisfies the original instruction and (ii) is the shortest path in the graph that can achieve the goal.

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

5. Then, The instruction path is executed by the target condition policy, which is part of the VNM. ​

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

Experimental results

Qualitative evaluation

Figure 4 shows some examples of paths taken by the robot (Note that the robot cannot obtain the image above the head and the spatial positioning of the feature points, and what is displayed is only the visual effect).

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

In Figure 4(a), LM-Nav is able to successfully locate simple feature points from its previous traversals and find a path to the goal. short path. Although there are multiple parking feature points in the environment, the objective function in Equation 3 enables the robot to select the correct parking feature point in the context, thereby minimizing the overall travel distance.

Figure 4(b) emphasizes the ability of LM-Nav to parse specified routes with multiple feature points—even though directly reaching the last feature point is the shortest route when ignoring the instruction path, the robot still A path that visits all feature points in the correct order can be found.

Use directives to disambiguate. Since the goal of LM Nav is to follow instructions, not just reach the final goal, different instructions may result in different traversals. Figure 5 shows an example where modifying instructions can disambiguate multiple paths to a goal. For shorter prompts (blue), LM Nav prefers the more direct path. When specifying a more fine-grained route (magenta), LM Nav takes alternative paths through different sets of feature points.

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

​The situation of missing feature points. Although LM-Nav can effectively parse feature points in instructions, locate them on the graph, and find the path to the goal, this process relies on the assumption that feature points (i) exist in the real environment, and (ii) can be recognized by VLM. Figure 4(c) shows a situation where the executable path fails to visit one of the feature points—a fire hydrant—and takes a path around the top of the building instead of the bottom. This failure case was due to the VLM's inability to detect fire hydrants from the robot's observations.

In independently evaluating the efficacy of VLM in retrieving feature points, the researchers found that although it is the best off-the-shelf model for this type of task, CLIP is unable to retrieve a small number of "hard" feature points, including Fire hydrants and cement mixers. But in many real-world situations, the robot can still successfully find a path to visit the remaining feature points.

Quantitative Evaluation

Table 1 summarizes the quantitative performance of the system in 20 instructions. In 85% of the experiments, LM-Nav was able to consistently follow instructions without collisions or detachments (an average of one intervention every 6.4 kilometers of travel). Compared to the baseline without navigation model, LM-Nav consistently performs better in executing efficient, collision-free target paths. In all unsuccessful experiments, the failure can be attributed to insufficient capabilities in the planning phase—the inability of the search algorithm to intuitively locate certain “hard” feature points in the graph—resulting in incomplete execution of instructions. An investigation of these failure modes revealed that the most critical part of the system is the VLM's ability to detect unfamiliar feature points, such as fire hydrants, and scenes under challenging lighting conditions, such as underexposed images.

Reinforcement learning guru Sergey Levines new work: Three large models teach robots to recognize their way

The above is the detailed content of Reinforcement learning guru Sergey Levine's new work: Three large models teach robots to recognize their way. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:51CTO.COM. If there is any infringement, please contact admin@php.cn delete
解读CRISP-ML(Q):机器学习生命周期流程解读CRISP-ML(Q):机器学习生命周期流程Apr 08, 2023 pm 01:21 PM

译者 | 布加迪审校 | 孙淑娟目前,没有用于构建和管理机器学习(ML)应用程序的标准实践。机器学习项目组织得不好,缺乏可重复性,而且从长远来看容易彻底失败。因此,我们需要一套流程来帮助自己在整个机器学习生命周期中保持质量、可持续性、稳健性和成本管理。图1. 机器学习开发生命周期流程使用质量保证方法开发机器学习应用程序的跨行业标准流程(CRISP-ML(Q))是CRISP-DM的升级版,以确保机器学习产品的质量。CRISP-ML(Q)有六个单独的阶段:1. 业务和数据理解2. 数据准备3. 模型

2023年机器学习的十大概念和技术2023年机器学习的十大概念和技术Apr 04, 2023 pm 12:30 PM

机器学习是一个不断发展的学科,一直在创造新的想法和技术。本文罗列了2023年机器学习的十大概念和技术。 本文罗列了2023年机器学习的十大概念和技术。2023年机器学习的十大概念和技术是一个教计算机从数据中学习的过程,无需明确的编程。机器学习是一个不断发展的学科,一直在创造新的想法和技术。为了保持领先,数据科学家应该关注其中一些网站,以跟上最新的发展。这将有助于了解机器学习中的技术如何在实践中使用,并为自己的业务或工作领域中的可能应用提供想法。2023年机器学习的十大概念和技术:1. 深度神经网

基于因果森林算法的决策定位应用基于因果森林算法的决策定位应用Apr 08, 2023 am 11:21 AM

译者 | 朱先忠​审校 | 孙淑娟​在我之前的​​博客​​中,我们已经了解了如何使用因果树来评估政策的异质处理效应。如果你还没有阅读过,我建议你在阅读本文前先读一遍,因为我们在本文中认为你已经了解了此文中的部分与本文相关的内容。为什么是异质处理效应(HTE:heterogenous treatment effects)呢?首先,对异质处理效应的估计允许我们根据它们的预期结果(疾病、公司收入、客户满意度等)选择提供处理(药物、广告、产品等)的用户(患者、用户、客户等)。换句话说,估计HTE有助于我

使用PyTorch进行小样本学习的图像分类使用PyTorch进行小样本学习的图像分类Apr 09, 2023 am 10:51 AM

近年来,基于深度学习的模型在目标检测和图像识别等任务中表现出色。像ImageNet这样具有挑战性的图像分类数据集,包含1000种不同的对象分类,现在一些模型已经超过了人类水平上。但是这些模型依赖于监督训练流程,标记训练数据的可用性对它们有重大影响,并且模型能够检测到的类别也仅限于它们接受训练的类。由于在训练过程中没有足够的标记图像用于所有类,这些模型在现实环境中可能不太有用。并且我们希望的模型能够识别它在训练期间没有见到过的类,因为几乎不可能在所有潜在对象的图像上进行训练。我们将从几个样本中学习

LazyPredict:为你选择最佳ML模型!LazyPredict:为你选择最佳ML模型!Apr 06, 2023 pm 08:45 PM

本文讨论使用LazyPredict来创建简单的ML模型。LazyPredict创建机器学习模型的特点是不需要大量的代码,同时在不修改参数的情况下进行多模型拟合,从而在众多模型中选出性能最佳的一个。 摘要本文讨论使用LazyPredict来创建简单的ML模型。LazyPredict创建机器学习模型的特点是不需要大量的代码,同时在不修改参数的情况下进行多模型拟合,从而在众多模型中选出性能最佳的一个。​本文包括的内容如下:​简介​LazyPredict模块的安装​在分类模型中实施LazyPredict

Mango:基于Python环境的贝叶斯优化新方法Mango:基于Python环境的贝叶斯优化新方法Apr 08, 2023 pm 12:44 PM

译者 | 朱先忠审校 | 孙淑娟引言模型超参数(或模型设置)的优化可能是训练机器学习算法中最重要的一步,因为它可以找到最小化模型损失函数的最佳参数。这一步对于构建不易过拟合的泛化模型也是必不可少的。优化模型超参数的最著名技术是穷举网格搜索和随机网格搜索。在第一种方法中,搜索空间被定义为跨越每个模型超参数的域的网格。通过在网格的每个点上训练模型来获得最优超参数。尽管网格搜索非常容易实现,但它在计算上变得昂贵,尤其是当要优化的变量数量很大时。另一方面,随机网格搜索是一种更快的优化方法,可以提供更好的

人工智能自动获取知识和技能,实现自我完善的过程是什么人工智能自动获取知识和技能,实现自我完善的过程是什么Aug 24, 2022 am 11:57 AM

实现自我完善的过程是“机器学习”。机器学习是人工智能核心,是使计算机具有智能的根本途径;它使计算机能模拟人的学习行为,自动地通过学习来获取知识和技能,不断改善性能,实现自我完善。机器学习主要研究三方面问题:1、学习机理,人类获取知识、技能和抽象概念的天赋能力;2、学习方法,对生物学习机理进行简化的基础上,用计算的方法进行再现;3、学习系统,能够在一定程度上实现机器学习的系统。

超参数优化比较之网格搜索、随机搜索和贝叶斯优化超参数优化比较之网格搜索、随机搜索和贝叶斯优化Apr 04, 2023 pm 12:05 PM

本文将详细介绍用来提高机器学习效果的最常见的超参数优化方法。 译者 | 朱先忠​审校 | 孙淑娟​简介​通常,在尝试改进机器学习模型时,人们首先想到的解决方案是添加更多的训练数据。额外的数据通常是有帮助(在某些情况下除外)的,但生成高质量的数据可能非常昂贵。通过使用现有数据获得最佳模型性能,超参数优化可以节省我们的时间和资源。​顾名思义,超参数优化是为机器学习模型确定最佳超参数组合以满足优化函数(即,给定研究中的数据集,最大化模型的性能)的过程。换句话说,每个模型都会提供多个有关选项的调整“按钮

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

AI Hentai Generator

AI Hentai Generator

Generate AI Hentai for free.

Hot Article

R.E.P.O. Energy Crystals Explained and What They Do (Yellow Crystal)
2 weeks agoBy尊渡假赌尊渡假赌尊渡假赌
Repo: How To Revive Teammates
4 weeks agoBy尊渡假赌尊渡假赌尊渡假赌
Hello Kitty Island Adventure: How To Get Giant Seeds
4 weeks agoBy尊渡假赌尊渡假赌尊渡假赌

Hot Tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

ZendStudio 13.5.1 Mac

ZendStudio 13.5.1 Mac

Powerful PHP integrated development environment

Safe Exam Browser

Safe Exam Browser

Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.

PhpStorm Mac version

PhpStorm Mac version

The latest (2018.2.1) professional PHP integrated development tool