Applied Planning, Learning, and Optimization (APOLLO) Lab
We develop algorithms for fast planning, learning, and optimization that allow robots and autonomous agents to continuously adapt as they operate, tightly integrating perception, action, and learning so systems can react quickly, gather the right information, and improve in real-time.
Our work is applied in home and assistive robotics, healthcare and robotic surgery, and disaster response.
Research Topics
Lab Space
Recent News
Two papers accepted at CoRL 2026
September 4, 2026
Two papers have been accepted to CoRL 2026! These papers are:
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Artificial Foveated Perception for Mitigating Shortcut Learning in Robotic Foundation Models (authors: Xiatao Sun, Yuan Zhuang, Mateo Sanchez Lopez Negrete, Matei-Victor Coldea, Chen Liang, Haoyang Zhang, Che Liu, Ziyao Zeng, Shawn Li, Qian Wang, Fei Miao, Daniel Rakita)
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Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient (authors: Haoxiang You, Yilang Liu, Davis Zong, Qian Wang, Teeratham Vitchutripop, Qi Wang, Daniel Rakita, Ian Abraham)
Paper accepted to the Journal of Robotic Surgery
August 1, 2026
Our paper, AI-Powered Semantic Segmentation Model for Enhanced Ureteral Mapping and Real-Time Instrument Feedback in Robotic Colorectal Surgery (authors: Justin M. Bader, Xiatao Sun, Tripp Rosenfelt, Alexis Ramirez-Hardy, Netanel Sapir, Rachel Scheub, Teeratham Vitchutripop, Amit Khanna, Haddon Pantel, Daniel Rakita) has been accepted for publication in the Journal of Robotic Surgery!
APOLLO Lab Hosts Robotics & AI Workshops for Yale Pathways to Science
July 8, 2026
In early July, APOLLO Lab PhD student TJ Vitchutripop led multiple workshop sessions on robotics and AI for the Yale Pathways to Science program.
Yale Pathways to Science is an outreach program designed for local high school students in the greater New Haven area to attend STEM-themed events and programming organized by the Yale community.
During our workshop, students were introduced to the critical problems that roboticists encounter day-to-day and the role deep learning techniques can play in tackling some of these issues. Students had the opportunity to participate in interactive activities, gain exposure to foundational mathematical structures, and see live robot demonstrations (dancing robot dogs included)!
Paper accepted at IROS 2026
June 17, 2026
Our paper, Robust and Efficient MuJoCo-based Model Predictive Control via Web of Affine Spaces Derivatives (authors: Chen Liang, Daniel Rakita) has been accepted to the International Conference on Intelligent Robots and Systems (IROS) 2026!