Student Talks

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 [...]