Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions

• URL: http://arxiv.org/abs/2406.19236v3
• Date: Sat, 02 Nov 2024 02:14:09 GMT
• Title: Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions
• Authors: Heng Li, Minghan Li, Zhi-Qi Cheng, Yifei Dong, Yuxuan Zhou, Jun-Yan He, Qi Dai, Teruko Mitamura, Alexander G. Hauptmann,
• Abstract summary: Vision-and-Language Navigation (VLN) aims to develop embodied agents that navigate based on human instructions. We introduce Human-Aware Vision-and-Language Navigation (HA-VLN), extending traditional VLN by incorporating dynamic human activities. We present the Expert-Supervised Cross-Modal (VLN-CM) and Non-Expert-Supervised Decision Transformer (VLN-DT) agents, utilizing cross-modal fusion and diverse training strategies.
• Score: 69.9980759344628
• License:
• Abstract: Vision-and-Language Navigation (VLN) aims to develop embodied agents that navigate based on human instructions. However, current VLN frameworks often rely on static environments and optimal expert supervision, limiting their real-world applicability. To address this, we introduce Human-Aware Vision-and-Language Navigation (HA-VLN), extending traditional VLN by incorporating dynamic human activities and relaxing key assumptions. We propose the Human-Aware 3D (HA3D) simulator, which combines dynamic human activities with the Matterport3D dataset, and the Human-Aware Room-to-Room (HA-R2R) dataset, extending R2R with human activity descriptions. To tackle HA-VLN challenges, we present the Expert-Supervised Cross-Modal (VLN-CM) and Non-Expert-Supervised Decision Transformer (VLN-DT) agents, utilizing cross-modal fusion and diverse training strategies for effective navigation in dynamic human environments. A comprehensive evaluation, including metrics considering human activities, and systematic analysis of HA-VLN's unique challenges, underscores the need for further research to enhance HA-VLN agents' real-world robustness and adaptability. Ultimately, this work provides benchmarks and insights for future research on embodied AI and Sim2Real transfer, paving the way for more realistic and applicable VLN systems in human-populated environments.

Related papers

• Sim-to-Real Transfer via 3D Feature Fields for Vision-and-Language Navigation [38.04404612393027]
  Vision-and-language navigation (VLN) enables the agent to navigate to a remote location in 3D environments following the natural language instruction. In this work, we propose a sim-to-real transfer approach to endow the monocular robots with panoramic traversability perception and panoramic semantic understanding. Our VLN system outperforms previous SOTA monocular VLN methods in R2R-CE and RxR-CE benchmarks within the simulation environments and is also validated in real-world environments.
  arXiv  Detail & Related papers  (2024-06-14T07:50:09Z)
• CoNav: A Benchmark for Human-Centered Collaborative Navigation [66.6268966718022]
  We propose a collaborative navigation (CoNav) benchmark. Our CoNav tackles the critical challenge of constructing a 3D navigation environment with realistic and diverse human activities. We propose an intention-aware agent for reasoning both long-term and short-term human intention.
  arXiv  Detail & Related papers  (2024-06-04T15:44:25Z)
• Continual Vision-and-Language Navigation [18.20829279972436]
  Vision-and-Language Navigation (VLN) agents navigate to a destination using natural language instructions and the visual information they observe. Existing methods for training VLN agents presuppose fixed datasets, leading to a significant limitation. We present the Continual Vision-and-Language Navigation (CVLN) paradigm, designed to evaluate agents trained through a continual learning process.
  arXiv  Detail & Related papers  (2024-03-22T09:15:36Z)
• NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning [101.56342075720588]
  Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions. Recent research has highlighted the promising capacity of large language models (LLMs) in VLN by improving navigational reasoning accuracy and interpretability. This paper introduces a novel strategy called Navigational Chain-of-Thought (NavCoT), where we fulfill parameter-efficient in-domain training to enable self-guided navigational decision.
  arXiv  Detail & Related papers  (2024-03-12T07:27:02Z)
• HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation [8.484737966013059]
  We introduce HabiCrowd, the first standard benchmark for crowd-aware visual navigation. Our proposed human dynamics model achieves state-of-the-art performance in collision avoidance. We leverage HabiCrowd to conduct several comprehensive studies on crowd-aware visual navigation tasks and human-robot interactions.
  arXiv  Detail & Related papers  (2023-06-20T08:36:08Z)
• Airbert: In-domain Pretraining for Vision-and-Language Navigation [91.03849833486974]
  Vision-and-language navigation (VLN) aims to enable embodied agents to navigate in realistic environments using natural language instructions. Recent methods explore pretraining to improve generalization of VLN agents. We introduce BnB, a large-scale and diverse in-domain VLN dataset.
  arXiv  Detail & Related papers  (2021-08-20T10:58:09Z)
• BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments [70.18430114842094]
  We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation. These activities are designed to be realistic, diverse, and complex. We include 500 human demonstrations in virtual reality (VR) to serve as the human ground truth.
  arXiv  Detail & Related papers  (2021-08-06T23:36:23Z)
• Visual Navigation Among Humans with Optimal Control as a Supervisor [72.5188978268463]
  We propose an approach that combines learning-based perception with model-based optimal control to navigate among humans. Our approach is enabled by our novel data-generation tool, HumANav. We demonstrate that the learned navigation policies can anticipate and react to humans without explicitly predicting future human motion.
  arXiv  Detail & Related papers  (2020-03-20T16:13:47Z)

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.