MSR Thesis Presentation

Abstract: Imitation learning has emerged as a leading paradigm for teaching manipulation skills to robots, but its success depends on the costly endeavour of collecting robot demonstrations through teleoperation. Generalizing to novel objects, environments, and task variations typically requires massive datasets that are expensive to scale. This thesis investigates an alternative lever: hierarchy—explicitly factorizing manipulation [...]

Abstract: Personalizing exoskeleton control to individual preferences is crucial for real world deployment. Data-driven approaches have enabled user-generalizable controllers, yet conventional personalization methods optimize biomechanical cost functions over user preferences. Prior work shows that users can perceive and report their preferred parameters, yet no lightweight method maps user intent to quantitative control parameter changes in [...]

Abstract: Robotic manipulation policies are typically evaluated on curated, in-distribution test sets, which offer limited insight into how these policies behave under plausible variation. One important source of this variation is geometric in nature, arising from small changes in object geometry that quietly alter grasp affordances and contact dynamics. Rather than treating robustness as a [...]