Student Speakers: Hojung Choi & Jonas Frey

Location: Gates B01

Time: Friday October 24, 3:00-4:00PM

Hojung Choi: General Compliant Robot Interaction Through Scalable F/T Sensing

Abstract: Robots excel at avoiding contact and performing structured tasks, but they often fail in unstructured, contact-rich environments. To interact safely and effectively, they must sense and regulate contact through compliance and tactile sensing. This talk presents two systems; CoinFT, a coin-sized, robust, and affordable 6-axis force/torque sensor, and UMI-FT, a handheld multimodal data collection platform that combines vision and finger-level force sensing. Together, they enable scalable tactile perception and compliant robot learning, allowing robots to not only detect contact but also use it, bringing us closer to general, contact-aware robot interaction with the real world.

Bio: Hojung Choi is a Postdoctoral Scholar in the Robotics and Embodied Artificial Intelligence Lab at Stanford University, advised by Professor Shuran Song. He received his Ph.D. in Mechanical Engineering from Stanford under Professor Mark Cutkosky. His research explores how robots can safely and effectively interact with the physical world through compliance and the sense of touch, integrating tactile sensing, multimodal learning, and robot design. He is a recipient of the Kwanjeong Fellowship.

Jonas Frey: Embodied Foundation Models: Bridging RL Locomotion and LLMs for Legged Navigation

Abstract: Quadrupedal robots trained with reinforcement learning (RL) can navigate rough and challenging environments without falling, yet they remain far from fully autonomous and fail to achieve general-purpose navigation. Consequently, current systems heavily rely on teleoperation, which limits their practical utility. Meanwhile, large language models (LLMs) have acquired broad world knowledge and reasoning capabilities. In this talk, we review what makes sim-to-real work for locomotion and why it remains limited for manipulation, examine the systemic challenges of integrating RL with reasoning models, and showcase recent work bridging the simulation diversity gap. We also evaluate current LLM capabilities for enabling navigation and propose paths forward for embodied LLMs conditioned on low-level RL policies.

Bio: Jonas Frey is a Joint Postdoctoral Researcher at Stanford’s Autonomous Systems Lab (Prof. Marco Pavone) and UC Berkeley’s BAIR (Prof. Jitendra Malik). His research focuses on learning perception and navigation for legged robots, with an emphasis on training RL policies and large behavior models. He earned his Ph.D. in robotics at the Robotic Systems Lab at ETH Zurich and the Max Planck Institute for Intelligent Systems, co-advised by Prof. Marco Hutter and Prof. Georg Martius. During his Ph.D., he was a visiting researcher at NASA’s Jet Propulsion Laboratory. Prior to his Ph.D., he worked as a Collaborative Robotics Engineer at SEW-Eurodrive and completed his Master’s in Robotics Systems and Control with distinction at ETH Zurich under the supervision of Prof. Roland Siegwart at the Autonomous Systems Lab.

Please visit https://stanfordasl.github.io/robotics_seminar/ for this quarter’s lineup of speakers. Although we encourage live in-person attendance, recordings of talks will be posted also.