Integrating Reinforcement Learning and Model Predictive Control for Autonomous Off-road Driving
Abstract: Safe and effective autonomous traversal of off-road terrain is challenging due to both terrain properties, such as low traction in sand or deformability of mud, and terrain geometries, including steep slopes, ditches, and uneven surfaces that can induce unsafe vehicle behaviors like excessive pitch and roll. Model Predictive Path Integral (MPPI) control provides a powerful [...]
Leveraging Deformations in Soft Objects and Robots for Contact-Rich Dexterity
Abstract: Robust dexterity depends on the ability to cause and reason about deformation. Robots may routinely need to interact with cables, dough, textiles, and hair in our homes, where deformation is the object state. Soft robots, in turn, are built from compliant materials in which deformation is not a by-product of interaction but its primary [...]
Longitudinal Human–Robot Interaction: Adaptive Personalization Across Repeated Encounters
Abstract: As robots increasingly move into homes, healthcare settings, and public environments, many are expected to support people not through single encounters, but through repeated interaction over time. In these settings, successful human--robot interaction depends not only on immediate task performance, but also on how users adapt to robotic systems, how expectations change with repeated [...]
Precise and Generalizable Robot Manipulation
Abstract: Robots in factories are still largely limited to structured environments with known object models. How can we bring robots into the more diverse, unstructured settings of our daily lives, where objects may vary widely in shape and appearance, while maintaining reliable performance? A popular direction today is to train generalist robot policies on large-scale internet [...]
WinkTPG: An Execution Framework for Multi-Agent Path Finding Using Temporal Reasoning
Abstract: Planning collision-free paths for a large group of agents is a challenging problem in many real-world applications. While recent advances in Multi-Agent Path Finding (MAPF) have shown promising progress, standard MAPF planners continue to rely on simplified kinodynamic models, preventing agents from directly following the generated MAPF plan. To bridge this gap, we propose [...]
Leveraging Local Models for Planning and Control with Contact
Abstract: Many planning and control approaches in robotics have converged on optimization-based formulations, with recent advances achieved by leveraging significant data and compute to attempt to tackle these nonlinear and non-convex problems. In this thesis, we instead focus on local models and demonstrate their benefits and surprising effectiveness. In the case of smooth optimization, the local [...]
Forecasting at Scale with Efficient Deep Learning Architectures
Abstract: Time Series Foundation Models (TSFMs) have scaled rapidly, with publicly reported pretraining corpora growing from 1.23 billion to 1 trillion data points between 2024 and 2026, an approximately 800× increase in two years. Recent work has further supplemented real-world data with synthetic data to expose models to broader time series patterns. Yet, this data-centric [...]
Think Globally, Solve Locally: Non-sequential Planning for Robotic Manipulation
Abstract: Robotic manipulation requires reasoning that bridges local competence over fine-grained dynamics with the construction of valid long-horizon plans. This mirrors human reasoning, where fast, automatic processes propose locally plausible actions from experience, while slower deliberation integrates them into a whole. Furthermore, evidence from cognitive science suggests that humans do not reason sequentially from start [...]
Leveraging Tactile Sensing to Resolve Uncertainty in Contact-Rich Manipulation
Abstract: Manipulation in agricultural and unstructured environments often involves contact-rich interactions with occluded objects. Most deployed systems treat contact as a hazard and rely on vision alone, which limits deployment from real-world field settings. This thesis adopts a different perspective: contact is a strategy for obtaining information where vision cannot provide. Touch can reveal additional [...]
Diffusion Temperature Sampling and Projective Ray Positional Encoding for Multi-view Attention
Abstract: Inductive biases have proven effective and often essential in the design of performant deep learning systems. This thesis presents two contributions that target distinct facets of how inductive biases can improve modern deep models. In Part 1, we focus on controlling the diversity--likelihood trade-off at inference-time of generative models. In Part 2, we study the [...]
Learning Bayesian Experimental Design Policies Efficiently
Abstract: Bayesian Experimental Design (BED) provides a principled framework for informative data collection, and is applied across domains as varied as adaptive clinical trials, ecological monitoring, hyperparameter optimization, and robotic search. Despite this broad applicability, BED methods remain difficult to deploy in practice: high-quality decision-making is computationally expensive, calibrated uncertainty estimation in sequential decision problems [...]
Quanta Perception as Probabilistic Events
Abstract: Autonomous systems ultimately rely on extracting information from light, yet remain brittle in extreme environments, from nighttime navigation to high-speed robotics. This limitation stems from a classical imaging abstraction: conventional sensors integrate photon flux over fixed exposure windows, imposing trade-offs between sensitivity, dynamic range, and temporal resolution that degrade perception when photons are scarce [...]