Robot Navigation in Unseen Spaces using an Abstract Map
Human navigation in built environments depends on symbolic spatial information which has unrealised potential to enhance robot navigation capabilities. Information sources such as labels, signs, maps, planners, spoken directions, and navigational gestures communicate a wealth of spatial information to the navigators of built environments; a wealth of information that robots typically ignore. We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans. The navigation system uses a novel data structure called the abstract map to imagine malleable spatial models for unseen spaces from spatial symbols. Sensorimotor perceptions from a robot are then employed to provide purposeful navigation to symbolic goal locations in the unseen environment. We show how a dynamic system can be used to create malleable spatial models for the abstract map, and provide an open source implementation to encourage future work in the area of symbolic navigation. Symbolic navigation performance of humans and a robot is evaluated in a real-world built environment. The paper concludes with a qualitative analysis of human navigation strategies, providing further insights into how the symbolic navigation capabilities of robots in unseen built environments can be improved in the future.
Code (0)
등록된 구현이 없습니다.
Tasks
NavigateRobot NavigationSimilar Papers 제목 키워드 기반
CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction
Real-life robot navigation involves more than just reaching a destination; it requires optimizing movements while addressing scenario-specific goals. An intuitive way for humans to express these goals is through abstract…
Imitation LearningNavigateRobot NavigationAPPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model
Autonomous navigation in highly constrained environments remains challenging for mobile robots. Classical navigation approaches offer safety assurances but require environment-specific parameter tuning; end-to-end learni…
Reinforcement LearningScene UnderstandingRobot NavigationWhat Is Near?: Room Locality Learning for Enhanced Robot Vision-Language-Navigation in Indoor Living Environments
Humans use their knowledge of common house layouts obtained from previous experiences to predict nearby rooms while navigating in new environments. This greatly helps them navigate previously unseen environments and loca…
Common Sense ReasoningDecision MakingDescriptiveNavigate+1Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning
Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes. While research on social navigation has focused mainly on the scalability wi…
Pose Estimationreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation
Existing object-search approaches enable robots to search through free pathways, however, robots operating in unstructured human-centered environments frequently also have to manipulate the environment to their needs. In…
Decision MakingHierarchical Reinforcement LearningNavigateObject