Persons with visual impairments (PwVI) have difficulties understanding and navigating spaces around them. Current wayfinding technologies either focus solely on navigation or provide limited communication about the environment. Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language. By understanding the commands from the user, DRAGON is able to guide the user to the desired landmarks on the map, describe the environment, and answer questions from visual observations. Through effective utilization of dialogue, the robot can ground the user's free-form descriptions to landmarks in the environment, and give the user semantic information through spoken language. We conduct a user study with blindfolded participants in an everyday indoor environment. Our results demonstrate that DRAGON is able to communicate with the user smoothly, provide a good guiding experience, and connect users with their surrounding environment in an intuitive manner.

Persons with Vision Impairments (PwVI) often have difficulties navigating indoor environments. The challenges and solutions can change based on their level of familiarity with the location. A collaborative effort was made to design a user needs assessment to understand the collaborative nature of human-robot interaction for wayfinding. The user study was an interview study to discuss with PwVI their navigation experience in familiar, somewhat familiar, and unfamiliar locations. Following this, we discussed their current solution strategies for wayfinding in those locations to discuss how they could imagine a robot to support wayfinding. We report on four case studies to illustrate specific user needs, such as vocal direction and orientation to learn a new environment and navigate, and highlight common strategies, such as supplemental lighting, different types of human assistance, and technologies used (i.e. white canes).

Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous methods deteriorates in partially observable environments and environments with dense crowds. To tackle these problems, we propose decentralized structural-Recurrent Neural Network (DS-RNN), a novel network that reasons about spatial and temporal relationships for robot decision making in crowd navigation. We train our network with model-free deep reinforcement learning without any expert supervision. We demonstrate that our model outperforms previous methods and successfully transfer the policy learned in the simulator to a real-world TurtleBot 2i.

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Collaborative robots require effective intention estimation to safely and smoothly work with humans in less structured tasks such as industrial assembly. During these tasks, human intention continuously changes across multiple steps, and is composed of a hierarchy including high-level interactive intention and low-level task intention. Thus, we propose the concept of intention tracking and introduce a collaborative robot system with a hierarchical framework that concurrently tracks intentions at both levels by observing force/torque measurements, robot state sequences, and tracked human trajectories. The high-level intention estimate enables the robot to both (1) safely avoid collision with the human to minimize interruption and (2) cooperatively approach the human and help recover from an assembly failure through admittance control. The low-level intention estimate provides the robot with task-specific information (e.g., which part the human is working on) for concurrent task execution. We implement the system on a UR5e robot, and demonstrate robust, seamless and ergonomic collaboration between the human and the robot in an assembly use case through an ablative pilot study.* denotes equal contribution as the first author. † denotes equal contribution as the second author.