Rachel Holladay
Rachel Holladay is the Asness Family Foundation Assistant Professor of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania, with a secondary appointment in the Department of Computer and Information Science. She is a member of the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory and directs the Autonomous Manipulation Lab. Her research focuses on enabling robots to perform long-horizon, contact-rich manipulation tasks in everyday environments by developing models and algorithms that exploit physics and geometry to address long-horizon decision-making and acting under uncertainty. She completed her Ph.D. in Electrical Engineering and Computer Science at MIT in 2024, advised by Tomás Lozano-Pérez and Alberto Rodriguez.
“On hardware, the paper shows cumulative success rates reaching 80% across three feedback iterations, with successes distributed across all three iterations rather than concentrated in the first (Figure 6, Section 5.2).”
Source→“The paper demonstrates this works as a front-end across three fundamentally different paradigms — Task and Motion Planning (TAMP), contact-implicit model predictive control (C3+), and the π0.5 vision-language-action model.”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.