9 accepted papers
Learning from human feedback is an effective way to improve robotic learning in exploration-heavy tasks. Compared to the wide application of binary human feedback, scalar human feedback has been used less because it is believed to be noisy and unstable. In this paper, we compare scalar and binary fe…
To enable robots to smoothly interact with humans during their travels together as a group, robots need the ability to adapt their motions under environmental changes and ensure all group members’ routes are feasible. To achieve this ability, robots require knowledge of the final destination and the…
Interactive Reinforcement Learning (RL) enables agents to learn from two sources: rewards taken from observations of the environment, and feedback or advice from a secondary critic source, such as human teachers or sensor feedback. The addition of information from a critic during the learning proces…
For robots to be perceived as full-fledged team members, they must display intelligent behavior along multiple dimensions. One challenge is that even when the robot and human are on the same team, the interaction may not feel like teamwork to the human. We present a novel algorithm, Teammate Algorit…
As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This pape
Human gaze is known to be a strong indicator of underlying human intentions and goals during manipulation tasks. This work studies gaze patterns of human teachers demonstrating tasks to robots and proposes ways in which such patterns can be used to enhance robot learning. Using both kinesthetic teac
Human-robot interaction research to date has investigated intent recognition and communication separately. In this paper, we explore the effects of integrating both the robot's ability to generate intentional motion and predict the human's motion in a collaborative physical task. We implemented an i…
Gaze provides subtle informative cues to aid fluent interactions among people. Incorporating human gaze predictions can signify how engaged a person is while interacting with a robot and allow the robot to predict a human's intentions or goals. We propose a novel approach to predict human gaze fixat…
Robots deployed for long periods of time need to be able to explore and learn from their environment. One approach to this problem has been reinforcement learning (RL), in which robots receive rewards from the environment that allow them to choose optimal actions. To speed learning when human superv…