David L. McPherson

Shuijing Liu, Aamir Hasan, Kaiwen Hong et al.

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. Videos and code are available at https://sites.google.com/view/dragon-wayfinding/home.

Shuijing Liu, Peixin Chang, Zhe Huang et al.

We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the intentions of people, which results in performance degradation. To learn a safe and efficient robot policy, we propose a novel recurrent graph neural network with attention mechanisms to capture heterogeneous interactions among agents through space and time. To encourage longsighted robot behaviors, we infer the intentions of dynamic agents by predicting their future trajectories for several timesteps. The predictions are incorporated into a model-free RL framework to prevent the robot from intruding into the intended paths of other agents. We demonstrate that our method enables the robot to achieve good navigation performance and non-invasiveness in challenging crowd navigation scenarios. We successfully transfer the policy learned in simulation to a real-world TurtleBot 2i. Our code and videos are available at https://sites.google.com/view/intention-aware-crowdnav/home.

Jesse Paterson, Jiwoong Han, Tom Cheng et al.

As the capability and complexity of UAVs continue to increase, the human-robot interface community has a responsibility to design better ways of specifying the complex 3D flight paths necessary for instructing them. Immersive interfaces, such as those afforded by virtual reality (VR), have several unique traits which may improve the user's ability to perceive and specify 3D information. These traits include stereoscopic depth cues which induce a sense of physical space as well as six degrees of freedom (DoF) natural head-pose and gesture interactions. This work introduces an open-source platform for 3D aerial path planning in VR and compares it to existing UAV piloting interfaces. Our study has found statistically significant improvements in safety and subjective usability over a manual control interface, while achieving a statistically significant efficiency improvement over a 2D touchscreen interface. The results illustrate that immersive interfaces provide a viable alternative to touchscreen interfaces for UAV path planning.

Kaylene C. Stocking, David L. McPherson, Robert P. Matthew et al.

Maximum likelihood constraint inference is a powerful technique for identifying unmodeled constraints that affect the behavior of a demonstrator acting under a known objective function. However, it was originally formulated only for discrete state-action spaces. Continuous dynamics are more useful for modeling many real-world systems of interest, including the movements of humans and robots. We present a method to generate a tabular state-action space that approximates continuous dynamics and can be used for constraint inference on demonstrations that obey the true system dynamics. We then demonstrate accurate constraint inference on nonlinear pendulum systems with 2- and 4-dimensional state spaces, and show that performance is robust to a range of hyperparameters. The demonstrations are not required to be fully optimal with respect to the objective, and the most likely constraints can be identified even when demonstrations cover only a small portion of the state space. For these reasons, the proposed approach may be especially useful for inferring constraints on human demonstrators, which has important applications in human-robot interaction and biomechanical medicine.

P. Dayani, N. Orr, A. Thomopoulos et al.

Recent advancements in radiation detection and computer vision have enabled small unmanned aerial systems (sUASs) to produce 3D nuclear radiation maps in real-time. Currently these state-of-the-art systems still require two operators: one to pilot the sUAS and another operator to monitor the detected radiation. In this work we present a system that integrates real-time 3D radiation visualization with semi-autonomous sUAS control. Our Virtual Reality interface enables a single operator to define trajectories using waypoints to abstract complex flight control and utilize the semi-autonomous maneuvering capabilities of the sUAS. The interface also displays a fused radiation visualization and environment map, thereby enabling simultaneous remote operation and radiation monitoring by a single operator. This serves as the basis for development of a single system that deploys and autonomously controls fleets of sUASs.

David L. McPherson, Kaylene C. Stocking, S. Shankar Sastry

When an expert operates a perilous dynamic system, ideal constraint information is tacitly contained in their demonstrated trajectories and controls. The likelihood of these demonstrations can be computed, given the system dynamics and task objective, and the maximum likelihood constraints can be identified. Prior constraint inference work has focused mainly on deterministic models. Stochastic models, however, can capture the uncertainty and risk tolerance that are often present in real systems of interest. This paper extends maximum likelihood constraint inference to stochastic applications by using maximum causal entropy likelihoods. Furthermore, we propose an efficient algorithm that computes constraint likelihood and risk tolerance in a unified Bellman backup, allowing us to generalize to stochastic systems without increasing computational complexity.

Paul Riggins, David McPherson

We propose the study of mathematical ludology, which aims to formally interrogate questions of interest to game studies and game design in particular. The goal is to extend our mathematical understanding of complex games beyond decision-making---the typical focus of game theory and artificial intelligence efforts---to explore other aspects such as game mechanics, structure, relationships between games, and connections between game rules and user-interfaces, as well as exploring related gameplay phenomena and typical player behavior. In this paper, we build a basic foundation for this line of study by developing a hierarchy of game descriptions, mathematical formalism to compactly describe complex discrete games, and equivalence relations on the space of game systems.

David L. McPherson, Dexter R. R. Scobee, Joseph Menke et al.

When a human supervisor collaborates with a team of robots, their 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.