Plan What You Can, Learn What You Must: Interleaving Planning and Learning for Multi-Robot Manipulation
Abstract: Multi-robot manipulation is becoming an inevitability of modern robotics. As hardware costs fall, the barrier to deploying robot teams has shifted from economics to algorithmic capability. To fulfill their promise, multi-robot systems must jointly reason about geometric coordination, contact interactions, task assignments, and scene dynamics, while adapting to variable team sizes and diverse robot [...]
To Be Announced
Robot Learning, With Inspiration From Child Development
Abstract: For intelligent robots to become ubiquitous, we need to “solve" locomotion, navigation and manipulation at sufficient reliability in widely varying environments. In locomotion, we now have demonstrations of humanoid walking in a variety of challenging environments. In navigation, we pursued the task of “Go to Any Thing” – a robot, on entering a newly [...]
A Layered Foundation for Reliable Trajectory Forecasting: Data, Evaluation, and Methods
Abstract: Reliable trajectory forecasting is a foundational requirement for autonomous robotic systems operating in environments with humans. Despite substantial progress in modeling techniques, existing forecasting systems often fail under distribution shift, exhibit socially implausible behaviors, or report misleading performance due to limitations in data coverage and evaluation practices. This thesis argues that reliable trajectory forecasting [...]
Learning Dynamic and Competitive Human Skills and Strategies for Animation and Robotics
Abstract: Humanoid control in animation and robotics requires physically realistic motion as well as the ability to adapt, coordinate actions over time, and make decisions in response to changing environments and other agents. Human motion data provides a powerful source of prior knowledge for learning natural and stable movement, but many existing approaches rely on [...]
RI Seminar with Vickie Webster-Wood
Advancing Spacecraft Autonomy: Optimal GNC, Vision-Based Estimation, and Systems Integration for Small Spacecraft
Abstract : Small spacecraft are increasingly expected to perform complex missions despite strict constraints in mass, power, and onboard computation. Meeting these demands requires advances in autonomy that enable effective decision-making, adaptive control, and robust state estimation within resource-limited platforms. This thesis develops optimization- and machine-learning–based methods to improve spacecraft autonomy across guidance, navigation, and [...]