Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches

• URL: http://arxiv.org/abs/2302.02898v1
• Date: Mon, 6 Feb 2023 16:06:07 GMT
• Title: Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches
• Authors: Linh K\"astner, Reyk Carstens, Christopher Liebig, Volodymyr Shcherbyna, Lena Nahrworld, Subhin Lee, Jens Lambrecht
• Abstract summary: We present Arena-Web, a web-based development and evaluation suite for developing, training, and testing DRL-based navigation planners. The interface is designed to be intuitive and engaging to appeal to non-experts and make the technology accessible to a wider audience.
• Score: 2.4937400423177767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, mobile robot navigation approaches have become increasingly important due to various application areas ranging from healthcare to warehouse logistics. In particular, Deep Reinforcement Learning approaches have gained popularity for robot navigation but are not easily accessible to non-experts and complex to develop. In recent years, efforts have been made to make these sophisticated approaches accessible to a wider audience. In this paper, we present Arena-Web, a web-based development and evaluation suite for developing, training, and testing DRL-based navigation planners for various robotic platforms and scenarios. The interface is designed to be intuitive and engaging to appeal to non-experts and make the technology accessible to a wider audience. With Arena-Web and its interface, training and developing Deep Reinforcement Learning agents is simplified and made easy without a single line of code. The web-app is free to use and openly available under the link stated in the supplementary materials.

Related papers

• Embodied Web Agents: Bridging Physical-Digital Realms for Integrated Agent Intelligence [109.32705135051486]
  Embodied Web Agents is a novel paradigm for AI agents that fluidly bridge the embodiment and web-scale reasoning.<n>We release the Embodied Web Agents Benchmark, which encompasses a diverse suite of tasks.<n>Results reveal significant performance gaps between state-of-the-art AI systems and human capabilities.
  arXiv  Detail & Related papers  (2025-06-18T17:58:17Z)
• Orca: Browsing at Scale Through User-Driven and AI-Facilitated Orchestration Across Malleable Webpages [18.25019078938821]
  We present novel interactions with our prototype web browser, Orca.<n>Orca supports user-driven exploration, operation, organization, and synthesis of web content at scale.<n>Our evaluation revealed an increased "appetite" for information foraging, enhanced user control, and more flexibility in sensemaking across a broader information landscape on the web.
  arXiv  Detail & Related papers  (2025-05-28T20:13:39Z)
• NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation [16.554282855005766]
  We present NavBench, a benchmark for training and evaluating Reinforcement Learning-based navigation policies.<n>Our framework standardizes task definitions, enabling different robots to tackle various navigation challenges.<n>By ensuring consistency between simulation and real-world deployment, NavBench simplifies the development of RL-based navigation strategies.
  arXiv  Detail & Related papers  (2025-05-20T15:48:23Z)
• WebThinker: Empowering Large Reasoning Models with Deep Research Capability [60.81964498221952]
  WebThinker is a deep research agent that empowers large reasoning models to autonomously search the web, navigate web pages, and draft research reports during the reasoning process.<n>It also employs an textbfAutonomous Think-Search-and-Draft strategy, allowing the model to seamlessly interleave reasoning, information gathering, and report writing in real time.<n>Our approach enhances LRM reliability and applicability in complex scenarios, paving the way for more capable and versatile deep research systems.
  arXiv  Detail & Related papers  (2025-04-30T16:25:25Z)
• WebNav: An Intelligent Agent for Voice-Controlled Web Navigation [0.0]
  WebNav is a voice-controlled web navigation agent that leverages a ReAct-inspired architecture and generative AI to provide this framework. Preliminary evaluations show that WebNav outperforms traditional screen readers in response time and task completion accuracy for the visually impaired.
  arXiv  Detail & Related papers  (2025-03-18T02:33:27Z)
• Biotic Browser: Applying StreamingLLM as a Persistent Web Browsing Co-Pilot [0.0]
  "Biotic Browser" is an innovative AI assistant leveraging StreamingLLM to transform web navigation and task execution. Characterized by its ability to simulate the experience of a passenger in an autonomous vehicle, the Biotic Browser excels in managing extended interactions and complex, multi-step web-based tasks.
  arXiv  Detail & Related papers  (2024-10-31T16:12:02Z)
• OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization [66.22117723598872]
  We introduce an open-source framework designed to facilitate the development of multimodal web agent. We first train the base model with imitation learning to gain the basic abilities. We then let the agent explore the open web and collect feedback on its trajectories.
  arXiv  Detail & Related papers  (2024-10-25T15:01:27Z)
• Hyp2Nav: Hyperbolic Planning and Curiosity for Crowd Navigation [58.574464340559466]
  We advocate for hyperbolic learning to enable crowd navigation and we introduce Hyp2Nav. Hyp2Nav leverages the intrinsic properties of hyperbolic geometry to better encode the hierarchical nature of decision-making processes in navigation tasks. We propose a hyperbolic policy model and a hyperbolic curiosity module that results in effective social navigation, best success rates, and returns across multiple simulation settings.
  arXiv  Detail & Related papers  (2024-07-18T14:40:33Z)
• Networking Systems for Video Anomaly Detection: A Tutorial and Survey [55.28514053969056]
  Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes. We showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD.
  arXiv  Detail & Related papers  (2024-05-16T02:00:44Z)
• NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration [57.15811390835294]
  This paper describes how we can train a single unified diffusion policy to handle both goal-directed navigation and goal-agnostic exploration. We show that this unified policy results in better overall performance when navigating to visually indicated goals in novel environments. Our experiments, conducted on a real-world mobile robot platform, show effective navigation in unseen environments in comparison with five alternative methods.
  arXiv  Detail & Related papers  (2023-10-11T21:07:14Z)
• Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments [2.194096102715421]
  Arena-Rosnav 2.0 is an extension to our previous works Arena-Bench and Arena-Rosnav. It adds a variety of additional modules for developing and benchmarking robotic navigation approaches.
  arXiv  Detail & Related papers  (2023-02-20T15:10:16Z)
• GNM: A General Navigation Model to Drive Any Robot [67.40225397212717]
  General goal-conditioned model for vision-based navigation can be trained on data obtained from many distinct but structurally similar robots. We analyze the necessary design decisions for effective data sharing across robots. We deploy the trained GNM on a range of new robots, including an under quadrotor.
  arXiv  Detail & Related papers  (2022-10-07T07:26:41Z)
• Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning [28.045441768064215]
  Reinforcement learning approaches have shown superior ability in solving sequential decision making problems. In this work, we consider the task of training an RL agent without employing the demonstration data. We propose to incorporate the hindsight experience replay (HER) and curriculum learning (CL) techniques with RL to efficiently learn the optimal navigation policy in the dense crowd.
  arXiv  Detail & Related papers  (2021-10-09T13:18:11Z)
• Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems [10.349425078806751]
  Deep reinforcement learning emerged as an alternative planning method to replace overly conservative approaches. Deep reinforcement learning approaches are not suitable for long-range navigation due to their proneness to local minima. In this paper, we propose a navigation system incorporating deep-reinforcement-learning-based local planners into conventional navigation stacks for long-range navigation.
  arXiv  Detail & Related papers  (2021-04-08T08:56:53Z)
• Adversarial Environment Generation for Learning to Navigate the Web [107.99759923626242]
  One of the bottlenecks of training web navigation agents is providing a learnable curriculum of training environments. We propose using Adversarial Environment Generation (AEG) to generate challenging web environments in which to train reinforcement learning (RL) agents. We show that the navigator agent trained with our proposed Flexible b-PAIRED technique significantly outperforms competitive automatic curriculum generation baselines.
  arXiv  Detail & Related papers  (2021-03-02T19:19:30Z)

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.