Sim-to-Real Transfer for Vision-and-Language Navigation

Abstract

We study the challenging problem of releasing a robot in a previously unseen environment, and having it follow unconstrained natural language navigation instructions. Recent work on the task of Vision-and-Language Navigation (VLN) has achieved significant progress in simulation. To assess the implications of this work for robotics, we transfer a VLN agent trained in simulation to a physical robot. To bridge the gap between the high-level discrete action space learned by the VLN agent, and the robot's low-level continuous action space, we propose a subgoal model to identify nearby waypoints, and use domain randomization to mitigate visual domain differences. For accurate sim and real comparisons in parallel environments, we annotate a 325m2 office space with 1.3km of navigation instructions, and create a digitized replica in simulation. We find that sim-to-real transfer to an environment not seen in training is successful if an occupancy map and navigation graph can be collected and annotated in advance (success rate of 46.8% vs. 55.9% in sim), but much more challenging in the hardest setting with no prior mapping at all (success rate of 22.5%).

CoRL 2020

References in corpus (6)

• On Evaluation of Embodied Navigation Agents
• Gated-Attention Architectures for Task-Oriented Language Grounding
• Retouchdown: Adding Touchdown to StreetLearn as a Shareable Resource for Language Grounding Tasks in Street View
• Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
• Language-guided Adaptive Perception with Hierarchical Symbolic Representations for Mobile Manipulators
• Cross-Lingual Vision-Language Navigation

Cited by in corpus (4)

• LanguageRefer: Spatial-Language Model for 3D Visual Grounding
• Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion
• Embodied Navigation at the Art Gallery
• Visual Perception Generalization for Vision-and-Language Navigation via Meta-Learning