Robots need models of human behavior for both inferring human goals and preferences, and predicting what people will do. A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward. While this model has been successful in a variety of robotics domains, its roots lie in econometrics, and in modeling decisions among different discrete options, each with its own utility or reward. In contrast, human trajectories lie in a continuous space, with continuous-valued features that influence the reward function. We propose that it is time to rethink the Boltzmann model, and design it from the ground up to operate over such trajectory spaces. We introduce a model that explicitly accounts for distances between trajectories, rather than only their rewards. Rather than each trajectory affecting the decision independently, similar trajectories now affect the decision together. We start by showing that our model better explains human behavior in a user study. We then analyze the implications this has for robot inference, first in toy environments where we have ground truth and find more accurate inference, and finally for a 7DOF robot arm learning from user demonstrations.

When a human supervisor collaborates with a team of robots, the human's attention is divided, and cognitive resources are at a premium. We aim to optimize the distribution of these resources and the flow of attention. To this end, we propose the model of an idealized supervisor to describe human behavior. Such a supervisor employs a potentially inaccurate internal model of the the robots' dynamics to judge safety. We represent these safety judgements by constructing a safe set from this internal model using reachability theory. When a robot leaves this safe set, the idealized supervisor will intervene to assist, regardless of whether or not the robot remains objectively safe. False positives, where a human supervisor incorrectly judges a robot to be in danger, needlessly consume supervisor attention. In this work, we propose a method that decreases false positives by learning the supervisor's safe set and using that information to govern robot behavior. We prove that robots behaving according to our approach will reduce the occurrence of false positives for our idealized supervisor model. Furthermore, we empirically validate our approach with a user study that demonstrates a significant (p = 0.0328) reduction in false positives for our method compared to a baseline safety controller.

In assistive teleoperation, an autonomous agent uses a prediction about a human user's intent to attempt to align the behavior of a controlled system with the human's goal, even if the human's own inputs are not perfectly aligned to that goal. Haptic Assistance achieves this effect by influencing the human through forces/torques applied to the human's control interface. In this work, we describe our method for creating such haptic assistance via Inverse Reinforcement Learning applied to successful task demonstrations. We then use our assistance method to examine the role that haptic feedback plays in assistive teleoperation. Through our user study, we find that when the assistance incorrectly predicts a user's intent, aiding the user via haptic feedback on their control interface, rather than directly modifying their input signal, is preferable and provides the user with a significantly greater sense of control over the system.

This paper presents the Push-Swap-Wait (PSW) algorithm, a scalable, decentralized and complete approach for multi-robot motion planning in confined spaces. The algorithm builds upon a “push and swap” paradigm that has been used effectively in centralized navigation. This push and swap approach was expanded to apply to decentralized planning by adding a waiting mode to handle situations in which communication between robots is lost. The completeness of the PSW algorithm can be guaranteed in cases where the environment can be modeled as a tree T for which the number of leaf nodes is greater than the number of robots navigating through it. The algorithm has a time complexity that is linear with the number of robots currently within communication, indicating that this algorithm is well suited for scaling to large systems of robots. To validate the PSW algorithm it was implemented successfully in multi-robot simulations and on hardware with four Dr. Robot Jaguar Lite Robots.