Toward Real-World Autonomous Off-Road Driving with Reinforcement Learning
Abstract: Off-road autonomous driving presents significant challenges such as navigating unmapped variable environments, traversing difficult terrain geometries such as steep slopes and ditches, and managing complex terrain dynamics. Addressing these challenges requires effective low-level adaptable control and long-horizon planning. Most existing methods utilize Model Predictive Control (MPC) methods such as Model Predictive Path Integral (MPPI), [...]
Explore and Exploit: Learning Policies for Efficient and Coordinated Active Search
Abstract: Robotic search is becoming a central capability in domains where the world is large, uncertain, and costly to inspect directly: search and rescue, environmental monitoring, surveillance, and infrastructure inspection. In these settings, the hard problem is not perception alone but the online sensing decision: where to look next as evidence arrives, while every motion [...]
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 [...]
Scalable Oversight Across Generative Visual AI: Toward Visual Storytelling for Everyone
Abstract: Generative visual AI has advanced by scaling data and compute, but its next bottleneck is oversight: the expert signals that evaluate, reward, and teach models what "good" looks like. Providing such oversight is increasingly difficult because foundation vision-language models now match or surpass most humans at the skills being judged. This thesis develops scalable [...]
Towards Fine-Grained Diagnosis of GUI Agents
Abstract: Graphical User Interface (GUI) agents need strong planning—what to do next—and grounding—where to click next—to solve user tasks. Yet these agents remain unreliable, and standard metrics such as task success or next-action accuracy often obscure why they fail. In this talk, I argue that reliable GUI agents require fine-grained diagnosis of core agentic capabilities, [...]
Embodied Design, Modeling, and Optimization for Bio-Inspired Aquatic Robots
Abstract: Bio-inspired aquatic robots offer a promising route to agile and efficient locomotion in fluid environments, where conventional rigid systems remain limited. In bio-inspired aquatic systems, locomotion is not determined by actuation or control alone, but instead emerges from tightly coupled interactions among body morphology, distributed compliance, actuation, onboard sensing, and the surrounding flow, making [...]
Integrating Learning and Collaboration for Human-Robot Alignment
Abstract: The alignment problem for robots considers how robots can learn to behave in accordance with human values. Today, robot learning paradigms enable humans to provide data (e.g., preference labels or demonstrations), which the robot uses to update its behavior (e.g., reward model or policy) to better align with human intentions. However, the current paradigm requires [...]
Towards Generalizable Embodied Navigation with Vision-Language Models
Abstract: Embodied navigation asks an autonomous agent to move through unknown environments and accomplish tasks such as finding objects or following instructions. Reliable performance in real-world settings, from household assistance to warehouse logistics, requires the agent to tightly integrate perception, semantic reasoning, and long-horizon planning under cluttered layouts, ambiguous appearances, and robot-specific constraints. Vision-language models [...]
From Margin to Center: Designing Inclusive & Equitable Service Robots with Disabled Adults
Abstract Service robots – autonomous systems that perform personal and professional tasks – have become a common sight in the Global North. In Human-robot interaction (HRI), researchers rarely consider the design implications of service robots for people with disabilities (PwDs) beyond controlled assistive contexts, such as private homes and assisted living facilities. Nevertheless, the purview [...]
Towards Socially Intelligent Multi-Agent Systems: Zero-Shot MARL Coordination and Theory-of-Mind Benchmarking of LLM Agents for Strategic Deception
Abstract: An agent that performs well on its own may still struggle when working with others. In multi-agent environments, success depends not only on understanding the world but also on understanding what other agents know, intend, and conceal. Cooperative partners follow hidden conventions, while adversarial opponents deceive. This work argues that robust multi-agent behavior requires [...]
Data-Driven Representation and Reasoning for Aviation Safety
Abstract: Aviation safety analysis increasingly benefits from large-scale operational trajectory data, yet raw motion traces alone are insufficient for understanding safety-critical events on the airport surface. The significance of an aircraft’s motion depends on the structured operational environment in which it occurs, including runways, taxiways, hold-short lines, and interactions among multiple agents over time. This [...]
Brain-Aligned Tactile Representations for Dexterous Robot Learning
Abstract: Touch is the essential sensory modality through which animals and robots physically negotiate the world. While much of robotic touch focuses on the capabilities of currently available tactile hardware, this thesis asks a more general question: what forms of tactile processing and representation could allow robots to approach the dexterity of animals? This thesis [...]
RI PhD Thesis Defense – Angela Chen
Date: June 26, 2026 Time: 10:00 – 11:30 AM Location: GHC – Room 4405 Zoom Link Type: Ph.D. Thesis Defense Who: Angela Chen Title: Behavioral Modeling of Interpersonal Dynamics as Controllable Agentic Systems: Empirically-grounded Adaptive Virtual Patients for Psychotherapy Training Abstract: The need for mental health care continues to outpace the supply of trained psychotherapists. Training is itself a bottleneck because supervision [...]
Spatial Reasoning and Semantic Representations for Autonomous Exploration and Object Search
Abstract: Autonomous robot exploration and object search in unknown environments are fundamental capabilities in robotics, with applications ranging from search and rescue to structural inspection. A central challenge in both tasks is that robots often must make decisions based on information they have not yet directly observed–reasoning about unexplored space, predicting future information gain, or [...]