Michelle Zhao

Hi, I am a fourth-year PhD student in the Robotics Institute at Carnegie Mellon University, working on human-robot collaboration. I am advised by Reid Simmons and Henny Admoni, and am part of the Human and Robot Partners (HARP) Lab and Reliable Autonomous Systems Lab (RASL). I am supported by the National Defense Science and Engineering Graduate (NDSEG) Fellowship.

My research interests lie in the areas of interactive machine learning, reinforcement learning, and human robot interaction. I graduated from Caltech ('20) with a degree in Computer Science and a minor in Information and Data Science, where I worked with Yuxin Chen and Yisong Yue on machine teaching and Soon-Jo Chung on distributed control.

Email  /  CV  /  Bio  /  Google Scholar  /  Twitter  /  Github

Research

I'm interested in developing algorithms to enable fluent human-robot coordination and collaboration. I currently work on projects focused on human-robot collaboration, mutual adaptation, and modeling human behavior.

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.

We propose a method 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.

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.

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.

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.

Workshop Papers

Best Poster Presentation Award

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.

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.

Other

Design by Jon Barron.