E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models

• URL: http://arxiv.org/abs/2409.10027v2
• Date: Wed, 2 Oct 2024 00:50:27 GMT
• Title: E2Map: Experience-and-Emotion Map for Self-Reflective Robot Navigation with Language Models
• Authors: Chan Kim, Keonwoo Kim, Mintaek Oh, Hanbi Baek, Jiyang Lee, Donghwi Jung, Soojin Woo, Younkyung Woo, John Tucker, Roya Firoozi, Seung-Woo Seo, Mac Schwager, Seong-Woo Kim,
• Abstract summary: Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions. Existing methods are primarily designed for static environments and do not leverage agent's own experiences to refine its initial plans. This study introduces the Experience-and-Emotion Map (E2Map), which not only integrates LLM knowledge but also the agent's real-world experiences.
• Score: 16.50787220881633
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have shown significant potential in guiding embodied agents to execute language instructions across a range of tasks, including robotic manipulation and navigation. However, existing methods are primarily designed for static environments and do not leverage the agent's own experiences to refine its initial plans. Given that real-world environments are inherently stochastic, initial plans based solely on LLMs' general knowledge may fail to achieve their objectives, unlike in static scenarios. To address this limitation, this study introduces the Experience-and-Emotion Map (E2Map), which integrates not only LLM knowledge but also the agent's real-world experiences, drawing inspiration from human emotional responses. The proposed methodology enables one-shot behavior adjustments by updating the E2Map based on the agent's experiences. Our evaluation in stochastic navigation environments, including both simulations and real-world scenarios, demonstrates that the proposed method significantly enhances performance in stochastic environments compared to existing LLM-based approaches. Code and supplementary materials are available at https://e2map.github.io/.

Related papers

• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• Multi-Object Navigation in real environments using hybrid policies [18.52681391843433]
  We introduce a hybrid navigation method, which decomposes the problem into two different skills. We show the advantages of this approach compared to end-to-end methods both in simulation and a real environment.
  arXiv  Detail & Related papers  (2024-01-24T20:41:25Z)
• Vision and Language Navigation in the Real World via Online Visual Language Mapping [18.769171505280127]
  Vision-and-language navigation (VLN) methods are mainly evaluated in simulation. We propose a novel framework to address the VLN task in the real world. We evaluate the proposed pipeline on an Interbotix LoCoBot WX250 in an unseen lab environment.
  arXiv  Detail & Related papers  (2023-10-16T20:44:09Z)
• LASER: LLM Agent with State-Space Exploration for Web Navigation [57.802977310392755]
  Large language models (LLMs) have been successfully adapted for interactive decision-making tasks like web navigation. Previous methods implicitly assume a forward-only execution mode for the model, where they only provide oracle trajectories as in-context examples. We propose to model the interactive task as state space exploration, where the LLM agent transitions among a pre-defined set of states by performing actions to complete the task.
  arXiv  Detail & Related papers  (2023-09-15T05:44:08Z)
• CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation [73.78984332354636]
  CorNav is a novel zero-shot framework for vision-and-language navigation. It incorporates environmental feedback for refining future plans and adjusting its actions. It consistently outperforms all baselines in a zero-shot multi-task setting.
  arXiv  Detail & Related papers  (2023-06-17T11:44:04Z)
• Navigating to Objects in the Real World [76.1517654037993]
  We present a large-scale empirical study of semantic visual navigation methods comparing methods from classical, modular, and end-to-end learning approaches. We find that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% simulation to 23% real-world success rate due to a large image domain gap between simulation and reality.
  arXiv  Detail & Related papers  (2022-12-02T01:10:47Z)
• Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents [111.33545170562337]
  We investigate the possibility of grounding high-level tasks, expressed in natural language, to a chosen set of actionable steps. We find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into low-level plans. We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions.
  arXiv  Detail & Related papers  (2022-01-18T18:59:45Z)
• Learning to Move with Affordance Maps [57.198806691838364]
  The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
  arXiv  Detail & Related papers  (2020-01-08T04:05:11Z)

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.