Several researchers, present authors included, envision personal mobile robot agents that can assist humans in their daily tasks. Despite many advances in robotics, such mobile robot agents still face many limitations in their perception, cognition, and action capabilities. In this work, we propose a symbiotic interaction between robot agents and humans to overcome the robot limitations while allowing robots to also help humans. We introduce a visitor’s companion robot agent, as a natural task for such symbiotic interaction. The visitor lacks knowledge of the environment but can easily open a door or read a door label, while the mobile robot with no arms cannot open a door and may be confused about its exact location, but can plan paths well through the building and can provide useful relevant information to the visitor. We present this visitor companion task in detail with an enumeration and formalization of the actions of the robot agent in its interaction with the human. We briefly describe the wifi-based robot localization algorithm and show results of the different levels of human help to the robot during its navigation. We then test the value of robot help to the visitor during the task to understand the relationship tradeoffs. Our work has been fully implemented in a mobile robot agent, CoBot, which has successfully navigated for several hours and continues to navigate in our indoor environment.

Abstract—This paper addresses some of the problems that arise when applying active learning to the context of human–robot interaction (HRI). Active learning is an attractive strategy for robot learners because it has the potential to improve the accuracy and the speed of learning, but it can cause issues from an interaction perspective. Here we present three interaction modes that enable a robot to use active learning queries. The three modes differ in when they make queries: the first makes a query every turn, the second makes a query only under certain conditions, and the third makes a query only when explicitly requested by the teacher. We conduct an experiment in which 24 human subjects teach concepts to our upper-torso humanoid robot, Simon, in each interaction mode, and we compare these modes against a baseline mode using only passive supervised learning. We report results from both a learning and an interaction perspective. The data show that the three modes using active learning are preferable to the mode using passive supervised learning both in terms of performance and human subject preference, but each mode has advantages and disadvantages. Based on our results, we lay out several guidelines that can inform the design of future robotic systems that use active learning in an HRI setting. Index Terms—Active learning, human–robot interaction. I.

As robots become incorporated into our environments, they must be equipped with the ability to communicate effectively with us. In particular, robots that perform longer tasks for a small set of people (e.g., a companion robot to escort visitors to meetings all day) need to be able to start and maintain interesting and relevant dialog with any and all humans involved. In this work, we present our ongoing work on our robot, CoBot, which is assigned an all-day task to escort a visitor around our building and perform tasks for her. We first describe CoBot’s dialog manager which is responsible for the task-oriented dialog, including dialog to meet the visitor’s needs, CoBot’s notifications of interesting locations around the building, and the robot’s own requests for help. We, then, focus two aspects of the dialog manager: 1) how CoBot can invoke more accurate answers to its requests for help from the visitor and 2) how to reduce repetitive dialog which can happen during all-day interactions. We provide an example dialog between CoBot and a visitor to illustrate the dialog manager’s capabilities.

When mobile robots perform tasks in environments with humans, it seems appropriate for the robots to rely on such humans for help instead of dedicated human oracles or supervisors. However, these humans are not always available nor always accurate. In this work, we consider human help to a robot as concretely providing observations about the robot’s state to reduce state uncertainty as it executes its policy autonomously. We model the probability of receiving an observation from a human in terms of their availability and accuracy by introducing Human Observation Providers POMDPs (HOP-POMDPs). We contribute an algorithm to learn human availability and accuracy online while the robot is executing its current task policy. We demonstrate that our algorithm is effective in approximating the true availability and accuracy of humans without depending on oracles to learn, thus increasing the tractability of deploying a robot that can occasionally ask for help.

We are interested in task-driven robots in our environments that can communicate with humans. While today’s robots often communicate with humans to overcome their limited perception and execution, the relationship between humans and robots is often one-sided in which the human is providing all the help to the robot without their own benefits. Instead, we propose a symbiotic relationship in which the robot performs tasks for humans and only ask for help to complete the task successfully. The symbiotic relationship is a more balanced one in which the robot and human mutually benefit each other through their actions and help. We introduce the Visitor-Companion Task for a robot to accompany a human visitor to meetings throughout the day as an example of the relationship and our robot, CoBot, that implements the task. We discuss both the planning requirements and benefits for a robot in a symbiotic relationship as well as the benefits and limitations of the humans.

In our work, a robot can proactively ask for help when necessary, based on its awareness of its sensing and actuation limitations. Approaches in which humans provide help to robots do not necessarily reason about the human availability and accuracy. Instead, we model the availability of humans in the robot’s environment and present a planning approach that uses such model to generate the robot navigational plans. In particular, we contribute two separate planners that allow a robot to distinguish actions that it cannot complete autonomously from ones that it can. In the first planner, the robot plans autonomous actions when possible and requests help to complete actions that it could not otherwise complete. Then for actions that it can perform autonomously, we use a POMDP policy that incorporates the human availability model to plan actions that reduce uncertainty or that increase the likelihood of the robot finding an available human to help it reduce its uncertainty. We have shown in prior work that asking people in the environment for help during tasks can reduce task completion time and increase the robot’s ability to perform tasks.

Robots are increasingly autonomous in our environments, but they still must overcome limited sensing, reasoning, and actuating capabilities while completing services for humans. While some work has focused on robots that proactively request help from humans to reduce their limitations, the work often assumes that humans are always available to help. In this work, we propose a model for task-embedded robot navigation that includes the people who can help the robot and benefit from the robot’s services- those who are assigned static locations in the environment, in particular offices. These occupants have different challenges compared to traditional helpers such as teleoperators in that they are not always available to help and they are spatially-situated and therefore physically cannot help in every location.