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.
Nathan is a researcher, athlete, writer, and mental-health advocate.
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SubscribeRobotics
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.
Research Experience
- Novel Robotics: I want to be able to build useful robots from whatever pieces an engineer has.
- Model-based Reinforcement Learning: I am optimistic about interpretable learning for robots.
- Robot Learning in Weak-sensor Environments: As a practical roboticist (or a data-scientist), I want to make systems that work in all parts of the world.
- Societal Implications of RL: starting with blogging, I have built a network of interdisciplinary scholars studying the societal impacts of reinforcement learning at internet-scale.
Nathan is a researcher, athlete, writer, and mental-health advocate.
Featured Posts:
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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.