BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-agent and Swarm Control
IEEE International Symposium on Multi-Robot and Multi-Agent Systems
A simulator for studying high-agent-count networked systems!
Research Experience & Interests
- Reinforcement Learning: As data-driven systems are more tightly integrated into society, more and more systems will show behavior as a feedback-based, iterative optimization. Lessons from RL will show us how these play out.
- Model-learning for Control: I am fascinated by how data reflects the dynamics of a system and how predicting that data can be used to solve tasks.
- Societally Beneficial AI: I have worked with sociologists, lawyers, and technical researchers to understand the normative and societal implications of automation and artificial intelligence.
- Novel & Other Robotics: I want to be able to build useful robots from whatever pieces an engineer has.
Robotics
Intelligent & novel devices to interact with the physical world.
Machine Learning
The science of using data to decide in the presence of uncertainty.
Society
Making sure the stakeholders of automation are in the conversation.
Axes for Sociotechnical Inquiry in AI Research
IEEE Transactions on Technology and Society
We present a concise set of directions for understanding the societal risks of new directions of AI research.
MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
An open-source PyTorch repository designed from the bottom up for model-based reinforcement learning research.
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
2021 International Conference on Artificial Intelligence and Statistics
We showed that advancements in AutoML when paired with common deep RL tasks, MBRL algorithms perform so well they break the simulator.
AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks
IEEE International Symposium on Technology and Society
We study three developing subfields of AI research and their growing relationship with the sociotechnical: AI Safety, Fair Machine Learning, and Human-in-the-loop Autonomy.
Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning
IEEE Robotics and Automation Letters
We explored how MBRL can learn multi-step, nonlinear controllers!
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Arxiv
Trying to reframe the MBRL framework with long-term predictions instead of one-step predictions!
Learning for Microrobot Exploration: Model-based Locomotion, Robust Navigation, and Low-Power Deep Classification
2020 Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)
A collections of steps towards a data-driven autonomous microrobot.
Objective Mismatch in Model-based Reinforcement Learning
2020 Conference on Learning for Decision and Control
Studying the numerical effects of a dual-optimization problem in model-based reinforcement learning -- control and dynamics. When optimizing model accuracy, there is no guarantee on improving task performance!
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
2020 IEEE International Conference on Robotics and Automation (ICRA)
Learning how to walk with a real-world hexapod using a hierarchy of model-free RL for basic motion primitives with model-based RL for higher level planning.
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
IEEE Robotics and Automation Letters
We used deep model-based reinforcement learning to have a quadrotor learn to hover from less than 5 minutes of all experimental training data.
Toward Controlled Flight of the Ionocraft: A Flying Microrobot Using Electrohydrodynamic Thrust With Onboard Sensing and No Moving Parts
IEEE Robotics and Automation Letters
A collection of steps towards controlled flight of The Ionocraft, a completely silent microrobot with ion thrust!
March 4, 2021
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Cornell Robotics Seminar (Virtual)
How can we use a better understanding of dynamics models to improve data-driven model predictive control (MPC).
[Watch Me][Slides]
April 30, 2020
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Practice Qualifying Exam
My practice quals talk! Learn about how I got into model-based reinforcement learning and what I wanted to do in the end of my Ph.D.
[Watch Me][Slides]
April 10, 2020
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Semiautonomous Seminar (UC Berkeley)
A mixed talk discussing the research challenges of controlling microrobots and how model-learning can be used to synthesize highly specific controllers.
[Watch Me][Slides]