PhD Thesis Proposal

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 [...]

Date: May 20th, 2026 Time: 3:00 - 4:30 pm Location: NSH 4305 Zoom Link  Type: RI PhD Thesis Proposal Who: Renos Zabounidis Title: Enforcing Neuro-Symbolic Structure in Deep Reinforcement Learning Abstract: Monolithic deep reinforcement learning trains a single network to learn vision, physics, planning, and control from reward alone. The result is poor sample efficiency, [...]

Date: May 21st, 2026 Time: 3:30 - 5:00 pm Room: NSH Room 4305 Zoom:  https://cmu.zoom.us/j/98318417145 Type: RI PhD Thesis Proposal Who: Anurag Ghosh Title: Scaling Long-Tailed Driving Perception and Planning with In-the-Wild Videos Abstract: Closed-loop driving, where methods produce actions a simulator reacts to, remains largely tied to driving logs from instrumented fleets. Thus, reliably driving in rare-but-critical scenarios is [...]

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 [...]

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 [...]

Abstract: Simulation enables robots to learn and evaluate behaviors at scale before real-world deployment. Yet the mismatch between simulation and the physical world remains a fundamental obstacle. This is particularly challenging for dexterous manipulation, where contact-rich interactions and dynamics variations across objects and robot embodiments are difficult to model. In my thesis research, I explore [...]

Abstract: Vision-language models have become remarkably capable on images and short videos, yet they still struggle with two abilities central to embodied intelligence: spatial understanding and long-range temporal reasoning. A major reason is representational: today's models process videos as long sequences of 2D patches, so computation grows with observation length even when the underlying scene [...]

RI CALENDAR EVENT Who: Shashwat Singh Type: RI Thesis Proposal Date: Wednesday, October 7, 2026 Time: 03:30PM - 05:00PM (ET) Location: NSH 4305 Zoom link: Here Title: Extending Robot Mobility Through Mechanical Adaptation Abstract: Unstructured terrain presents diverse challenges to robot locomotion, including obstacles, deformable surfaces, and confined passages. Terrain conditions also change with time, as [...]