TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Papers/Vision-and-Language Navigation: Interpreting visually-grou...

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, Anton Van Den Hengel

2017-11-20CVPR 2018 6Visual NavigationReinforcement LearningVision and Language NavigationTranslationVisual Question Answering (VQA)Visual Question Answering
PaperPDFCodeCode(official)CodeCodeCodeCodeCodeCode

Abstract

A robot that can carry out a natural-language instruction has been a dream since before the Jetsons cartoon series imagined a life of leisure mediated by a fleet of attentive robot helpers. It is a dream that remains stubbornly distant. However, recent advances in vision and language methods have made incredible progress in closely related areas. This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering. Both tasks can be interpreted as visually grounded sequence-to-sequence translation problems, and many of the same methods are applicable. To enable and encourage the application of vision and language methods to the problem of interpreting visually-grounded navigation instructions, we present the Matterport3D Simulator -- a large-scale reinforcement learning environment based on real imagery. Using this simulator, which can in future support a range of embodied vision and language tasks, we provide the first benchmark dataset for visually-grounded natural language navigation in real buildings -- the Room-to-Room (R2R) dataset.

Results

TaskDatasetMetricValueModel
Visual NavigationR2Rspl0.18Seq2Seq baseline

Related Papers

CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning2025-07-18VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning2025-07-17Spectral Bellman Method: Unifying Representation and Exploration in RL2025-07-17Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback2025-07-17VAR-MATH: Probing True Mathematical Reasoning in Large Language Models via Symbolic Multi-Instance Benchmarks2025-07-17QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation2025-07-17Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities2025-07-17Autonomous Resource Management in Microservice Systems via Reinforcement Learning2025-07-17