Uncertainty quantification offers a way for the learner (i.e. robot) to contend with distribution shifts encountered during deployment by actively seeking additional feedback from an expert (i.e. human) online. From the conformal prediction side, we introduce a novel uncertainty quantification algorithm called intermittent quantile tracking (IQT) that leverages a probabilistic model of intermittent labels, maintains asymptotic coverage guarantees, and empirically achieves desired coverage levels. From the interactive IL side, we develop ConformalDAgger, a new approach wherein the robot uses prediction intervals calibrated by IQT as a reliable measure of deployment-time uncertainty to actively query for more expert feedback.

Assistive robotic arms often have more degrees-of-freedom than a human teleoperator can control with a low-dimensional input, like a joystick. To overcome this challenge, existing approaches use data-driven methods to learn a mapping from low-dimensional human inputs to high-dimensional robot actions. However, determining if such a black-box mapping can confidently infer a user's intended high-dimensional action from low-dimensional inputs remains an open problem. Our key idea is to adapt the assistive map at training time to additionally estimate high-dimensional action quantiles, and then calibrate these quantiles via rigorous uncertainty quantification methods.

Part of such coordination involves ensuring that people have a good understanding of how a robot will act in the environment. This can be achieved through explanations of the robot’s policy. In this work, we investigate how to generate multi-agent strategy explanations for human-robot collaboration. We formulate the problem using a generic multi-agent planner, show how to generate visual explanations through strategy-conditioned landmark states and generate textual explanations by giving the landmarks to an LLM. Through a user study, we find that when presented with explanations from our proposed framework, users are able to better explore the full space of strategies and collaborate more efficiently with new robot partners.

Effective teams align their actions to optimize task performance while satisfying each team member’s constraints to the greatest extent possible. We propose a framework for representing human and robot contribution constraints in collaborative human-robot tasks. Additionally, we present an approach for learning a human partner’s contribution constraint online during a collaborative interaction. We evaluate our approach using a variety of simulated human partners in a collaborative decluttering task. Our results demonstrate that our method improves team performance over baselines with some, but not all, simulated human partners.

This work autonomously recognizes available task-completion strategies by observing human-human teams performing a collaborative task. By transforming team actions into low dimensional representations using hidden Markov models, we can identify strategies without prior knowledge. Robot policies are learned on each of the identified strategies to construct a Mixture-of-Experts model that adapts to the task strategies of unseen human partners.

This paper presents a framework for defining artificial intelligence (AI) that adapts to individuals within a group, and discusses the technical challenges for collaborative AI systems that must work with different human partners. Collaborative AI is not one-size-fits-all, and thus AI systems must tune their output based on each human partner's needs and abilities.

We show by learning the set of hidden states representing a team’s observed collaborative process behaviors over time, we both learn information about the team’s collective intelligence (CI), predict how CI will evolve in the future, and suggest when an agent might intervene to improve team performance.

While today’s robot learning algorithms increasingly enable people to teach robots via diverse forms of feedback, they place the burden of responsibility on the human to perfectly understand what the robot doesn’t know and provide the “right” data. My research contends that robots should be proactive participants— they should bear some of the burden of knowing when they don’t know and should ask for targeted help. I tackle this problem by extending foundational uncertainty quantification techniques to the HRI setting, enabling robots to rigorously “know when they don’t know” even when relying on black-box policies and to ask for strategic help.

We consider the problem of teaching a human partner a joint reward function, which captures how both human and robot should contribute to the task. This reward, which is known only to the robot, is joint over human and robot actions, and encompasses constraints over how the human and robot should contribute to a task. By adapting existing machine teaching frameworks for our collaborative domain, we seek to provide a minimal number of demonstrations such that a human can learn the rewards.

Best Poster Presentation Award

We propose a method for generating a customizable quantity of synthetic data that reflects the variability in task execution styles seen in the real-world task, and enables us to train a baseline sequential model that predicts the next action a participant will take within a cooking activity.

We present a closed-loop interaction framework that adapts the level of information complexity based on the human partner’s observable cognitive understanding. This work investigates how knowledge and preparation impact the suitability of di erent complexity levels, motivating dynamic interaction.