We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work.

Teleoperated avatar robots allow people to transport their manipulation skills to environments that may be difficult or dangerous to work in. Current systems are able to give operators direct control of many components of the robot to immerse them in the remote environment, but operators still struggle to complete tasks as competently as they could in person. We present a framework for incorporating open-world shared control into avatar robots to combine the benefits of direct and shared control. This framework preserves the fluency of our avatar interface by minimizing obstructions to the operator's view and using the same interface for direct, shared, and fully autonomous control. In a human subjects study (N=19), we find that operators using this framework complete a range of tasks significantly more quickly and reliably than those that do not.

While there are many examples in which robots provide social assistance, a lack of theory on how the robots should decide how to assist impedes progress in realizing these technologies. To address this deficiency, we propose a pair of computational models to guide a robot as it provides social assistance. The model of social autonomy helps a robot select an appropriate assistance that will help with the task at hand while also maintaining the autonomy of the person being assisted. The model of social alliance describes how a to determine whether the robot and the person being assisted are cooperatively working towards the same goal. Each of these models are rooted in social reasoning between people, and we describe here our ongoing work to adapt this social reasoning to human-robot interactions. Socially assistive robots (SARs) provide social assistance instead of physically intervening.

Conference calls represent a natural but limited communication channel between people. Lack of visual contact and limited bandwidth impoverish social cues people typically use to moderate their behavior. This paper presents a system capable of providing timely aural feedback enabling meeting participants to check themselves. The system is able to sense and recognize problems, reason about them, and make decisions on how and when to provide feedback based on an interaction policy. While a hand-crafted policy based on expert insight can be used, it is non-optimal and can be brittle. Instead, we use reinforcement learning to build a system that can adapt to users by interacting with them. To evaluate the system, we first conduct a user study and demonstrate its utility in getting meeting participants to contribute more equally. We then validate the adaptive feedback policy by demonstrating the agent's ability to adapt its action choices to different types of users.

Learning to provide appropriate assistance to people indifferent situations is an extremely important, but insufficientlyinvestigated machine learning task. Applications includehuman-robot and human-computer interactions settings to maximizing the benefits of assistive technologies. Three key challenges must be overcome to appropriately address this task: Complexity: the space of possible assistive policies can be very large, making many existing methods (e.g., fromreinforcement learning) too data inefficient to be practical. Noise and misspecification: observed human behavior is often noisy and parametric formulations that reduce complexity will typically suffer from model misspecification,leading to unboundedly sub-optimal assistance. Biasedness: data available for learning a model is biased by previously provided assistive actions, violating the typical assumptions of supervised learning. We develop a general framework for learning to assist in single intervention settings. The framework narrows the search for effective assistance by viewing previous behavior under assistance through a restricted set of statistics. Assistive policies for the worst-case context-assistance-outcome relationships satisfying these statistics are obtained. We embed the problem of learning how to assist users in cursor based target pointing tasks into this framework and outline its usage.