For the summer of 2018 I interned at Facebook Reality Labs. For 2015-2017 I was one of the lead software developers for the planning and control systems on our MIT-Draper team for the DARPA FLA (Fast Lightweight Autonomy) program.
Lucas Manuelli, Yunzhu Li, Pete Florence, Russ Tedrake. Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning. In Under Review
[ pdf ] [ site with video ]
Peter Florence. Dense Visual Learning for Robot Manipulation. PhD thesis, Massachusetts Institute of Technology, September 2019. [ pdf ] [ presentation video ]
Peter Florence, Lucas Manuelli, Russ Tedrake. Self-Supervised Correspondence in Visuomotor Policy Learning. In IEEE Robotics and Automation Letters (RA-L) and IEEE Conference on Robotics and Automoation (ICRA), November 2019 and May 2020
[ pdf ] [ site ] [ video ]
Lucas Manuelli*, Wei Gao*, Peter Florence, Russ Tedrake. kPAM: KeyPoint Affordances for Category-Level Robotic Manipulation. In International Symposium on Robotics Research (ISRR), Hanoi, Vietnam, October 2019
[ pdf ] [ site with videos ]
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, Steven Lovegrove. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019
[ pdf ] [ video ] [ code ] [ talk ]
Best Paper Finalist, CVPR 2019 Oral Presentation, CVPR 2019
Peter R. Florence*, Lucas Manuelli*, and Russ Tedrake. Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation. In Conference on Robot Learning (CoRL), Zurich, Switzerland, October 2018 [ pdf ] [ video ] [ code ]
Winner, Best Paper Award, CoRL 2018 Winner, Best Technical Paper, 2018 Amazon Robotics Best Paper Awards in Manipulation
Press: [ WIRED ] [ CNN ] [ Newsweek ] [ Engadget ] [ VentureBeat ] [ more ]
Pat Marion*, Peter R. Florence*, Lucas Manuelli*, and Russ Tedrake. LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes. In International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 2018. [ website with code, video, paper ]
Finalist, Best Vision Paper Award, ICRA 2018
Andrew J. Barry, Peter R. Florence, and Russ Tedrake. High-speed autonomous obstacle avoidance with pushbroom stereo. In Journal of Field Robotics (JFR), 2018. [ pdf ] [ video ]
Peter R. Florence, John Carter, and Russ Tedrake. Integrated perception and control at high speed: Evaluating collision avoidance maneuvers without maps. In WAFR: Workshop on the Algorithmic Foundations of Robotics, 2016. [ pdf ] [ presentation ]
Benoit Landry, Robin Deits, Peter R. Florence, and Russ Tedrake. Aggressive quadrotor flight through cluttered environments using mixed integer programming. In International Conference on Robotics and Automation (ICRA), Stockhom, Sweden, May 2016. [ pdf ][ video ]
William E. McClain, Peter R. Florence, Andrew Shu, Antoine Kahn, Jeffrey Schwartz, “Surface Dipole Engineering for Conducting Polymers,” In Organic Electronics, Volume 14, Issue 1, January 2013, Pages 411-415.
(Pending) Peter Raymond Florence, Christopher David Sachs, Kent W. Ryorchuk. Systems and methods for probabilistic semantic sensing in a sensory network. WO2015134879 A1 (2014)
Christopher David Sachs, Peter Raymond Florence. Dynamic spatially-resolved lighting using composited lighting models. U.S. Patent No. 9763306 (2014)
Lucas Manuelli, Peter R. Florence. Reinforcement Learning for Autonomous Driving Obstacle Avoidance using LIDAR. [ pdf ] [ video ] (2015)
Engineering Education for Kids
My friend Andy Barry and I started Stage One Education together. We develop and teach hands-on engineering workshops for kids across the country. Over 6,000 students to date have taken our workshops.
I have recently been into PyTorch:
Example from numpy to PyTorch: bilinear interpolation [ code ]
Another example from numpy to PyTorch: sampling dense correspondences [ code ]
TAing Underactuated Robotics (6.832) at MIT, Spring 2018, taught by my advisor Russ.