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 [...]

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 [...]

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 [...]

Abstract: Modern foundation models have achieved remarkable progress by learning broad physical and semantic common sense from large-scale data. However, robots operating in open-ended environments require more than general knowledge alone: they must continually specialize in new tasks, environments, and experiences encountered during deployment. Given only limited deployment-time data, how can robots learn to solve substantially more—and [...]