About Me:
I am a Ph.D. Candidate in the Aerospace Department (GALCIT) at the California Institute of Technology, advised by Prof. Soon-Jo Chung in the Computing and Mathematical Sciences Department. I received the B.S. in Mechanical Engineering from Stanford University in 2017, and the M.S. in Aeronautics from Caltech in 2018. I work at the intersection of planning, machine learning, and dynamical systems with applications in robotics, space autonomy, and self-driving cars.

I am on the 2023/2024 academic job market.

This is me!

Research Statement:

What do smart robots dream of? My research seeks answers to this question by designing how robots simulate the effect of their actions on the future, and how they use that information to make intelligent decisions. This is formalized in a search-based framework that (i) solves a new class of decision-making problems in real-time, (ii) provides explainability and theoretical guarantees, and (iii) synergizes with deep learning in a "dual-process" combination. My vision is to develop robot decision-making to explore and execute intelligent behavior on-the-fly, rather than relying on human intervention, specific pretraining, or prescriptive solutions.

News:
In Review (* denotes equal contribution):
This is SETS!
Bringing Games to Life: Decision-Making for Dynamical Systems with Spectral Expansion Tree Search. B. Riviere*, J. Lathrop*, S-J. Chung. Uses descriptive features from dynamical systems theory to simplify reasoning at a high level of generality in the physical world, enabling real-time decision-making for dynamical robots. In review at Science Robotics, please contact me if you are interested in a preprint.

This is sFEAST!
Dreaming to Disambiguate: Safe Fault Estimation via Active Sensing Tree Search J. Ragan, B. Riviere, S-J. Chung. Selects actions to excite observations most useful in diagnosing a fault affecting a robot's sensors and actuators, while probabilistically ensuring safety over the planning horizon. In review at Science Robotics, please contact me if you are interested in a preprint.

Selected Publications (* denotes equal contribution):
This is POMCPMF!
Bayesian Active Sensing for Fault Estimation with Belief Space Tree Search. J. Ragan*, B. Riviere*, S-J. Chung. Restore strategic exploration in information gathering tree search by computing exact Bayesian transitions, enabled by belief distribution factorization in fault estimation problems. Accepted at AIAA SciTech Forum 2023. Paper. Best Graduate Student Paper Award in the field of Guidance, Navigation, and Control at the 2023 AIAA SciTech Forum.

This is NTE!
Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments . B. Riviere, W. Hoenig, M. Anderson, S-J. Chung. Bias tree search with learned, decentralized neural networks for real-time game-theoretic planning in high dimensional continuous spaces. Accepted at IEEE Robotics and Automation Letters (Robotics and Automation Letters) and International Conference on Intelligent Robots and Systems (IROS) 2021. Arxiv, Code, Video

This is GLAS!
Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning. B. Riviere, W. Hoenig, Y. Yue, S-J. Chung. Learn decentralized coordination from centralized data and use nonlinear stability to enable end-to-end training with safety guarantees. Accepted at IEEE Robotics and Automation Letters and IROS 2020. Arxiv, Code, Video Honorable Mention for Best Paper of IEEE Robotics and Automation Letters 2020

This is HTD2!
Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch. B. Riviere, S-J. Chung. Coordinate thousands of urban taxis with decentralized Kalman filtering for information sharing, local online reinforcement learning for value estimation, and game-theoretic task assignment for coordination. Accepted at IEEE Transactions on Intelligent Transportation Systems 2021. Arxiv