Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
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 [...]
User Intent-Driven and Context-Aware Personalization for Assistive Exoskeletons
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 [...]
Design and Evaluation of Low-Cost, Open-Source Haptic Interfaces for Diverse Learning Applications
Abstract: Touch is a powerful yet underused channel for learning. Prior research shows that haptic interaction can support both sensorimotor skill acquisition and the understanding of abstract concepts by grounding learning in bodily experience. However, most haptic devices remain expensive, technically complex, and difficult to reproduce, which keeps them largely confined to specialized laboratories. This limits [...]
Scaling Sim2Real Learning for Robot Manipulation
Abstract: Recent progress in robot learning has led to impressively capable manipulation systems. Much of the progress has come from scaling up human demonstrations; however, collecting such data through manual teleoperation is slow, costly, and hard to scale. Physics-based Simulation offers a scalable, safe, and efficient alternative for generating large demonstration datasets. However, some core [...]
Annotation-Free Learning for Mobile Robot Navigation in Unstructured Environments
Abstract: Navigation in unstructured environments is a capability critical to many robotics applications such as forestry, construction, disaster response and defense. In these domains, robots have the potential to eliminate much of the dull, dirty and/or dangerous work that is currently performed by humans. Unfortunately, these environments pose a unique set of challenges for navigation not [...]
Autonomous Crop Manipulation: From Model-Based Reasoning to Learned Interaction
Abstract: Robots that manipulate crops must contend with plants that occlude themselves, deform under contact, and make the manipulator contact structures it neither targets nor sees in advance. This thesis argues that autonomous manipulation of crops in unstructured agricultural environments is best advanced not by choosing between model-based and learned approaches, but by integrating them [...]
Red and Blue Teaming for Robust Manipulation under Geometric Variations
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 [...]
Integrating Structured Knowledge for State and Geometry Estimation
Abstract: Reliable state and geometry estimation from limited observations is a fundamental challenge in robotics and perception. Observations are often noisy, partial, or ambiguous, making estimation ill-posed without additional structure. This thesis argues that robust estimation in these regimes is enabled by integrating structured knowledge into the inference process. Estimation can be viewed as inferring [...]
Physical Process-Informed Mapping for Robotic Exploration
Abstract: Mobile robots used for information gathering tasks rely on dense, predictive mapping of large-scale regions to determine where to take measurements. Current approaches to mapping commonly rely on Gaussian process regression to spatially correlate data, extrapolate from sparse samples, and estimate uncertainty. However, these approaches do not incorporate meaningful information about physical processes that [...]
Aligning Observations Across Viewpoint, Time, and Embodiment for Agricultural Perception and Manipulation
Abstract: Agricultural specialists are actively turning to robotic and computer vision-based systems to reduce the manual labor required to inspect and manipulate crops. These tasks require robots to perceive and interact with plants from partial, localized observations, often in dense and cluttered environments. For perception, a central challenge is that crops are small, are easily [...]