NEW I am excited to announce that I will be starting as an assistant professor at CMU in the Robotics Institute in Fall 2017!

Research Interests

My research lies at the intersection of robotics, machine learning, and computer vision.

I am interested in developing methods for robotic perception and control that can allow robots to operate in in the messy, cluttered environments of our daily lives. My approach is to design new deep learning / machine learning algorithms to understand environmental changes: how dynamic objects in the environment can move and how to affect the environment to achieve a desired task.

I have applied this idea of learning to understand environmental changes to improve a robot's capabilities in two domains: object manipulation and autonomous driving. I am currently working on learning to control indoor robots for various object manipulation tasks, dealing with questions about multi-task learning, robust learning, simulation to real-world transfer, and safety. Within autonomous driving, I have shown how, by modeling object appearance changes, we can improve a robot's capabilities for every part of the robot perception pipeline: segmentation, tracking, velocity estimation, and object recognition. By teaching robots to understand and affect environmental changes, I hope to open the door to many new robotics applications, such as robots for our homes, assisted living facilities, schools, hospitals, or disaster relief areas.

About Me

Starting Fall 2017, I will be working as an assistant professor at CMU in the Robotics Institute. I am currently working as a post-doc with Pieter Abbeel as part of the Berkeley Artificial Intelligence Research Laboratory. I completed my Ph.D. in computer science at Stanford working with Sebastian Thrun and Silvio Savarese. Prior to that I did research at the Weizmann Institute, worked in industry as a software developer, and received a B.S. and M.S. in mechanical engineering at MIT.

You can also download my CV.

  • Best Master's Thesis of 2012 in Stanford's Computer Science Department
    M.S. Thesis: "Autonomous Driving: Car Detection, Tracking, and Street Sign Detection,"
    co-advised by Vaughan Pratt and Sebastian Thrun.
Reverse Curriculum Generation for Reinforcement Learning
Carlos Florensa, David Held, Markus Wulfmeier, Pieter Abbeel

Automatic Goal Generation for Reinforcement Learning Agents
David Held*, Xinyang Geng*, Carlos Florensa*, Pieter Abbeel

  • 2017
  • Policy Transfer via Modularity
    Ignasi Clavera*, David Held*, Pieter Abbeel
    International Conference on Intelligent Robots and Systems (IROS), 2017

    Constrained Policy Optimization
    Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel
    International Conference on Machine Learning (ICML), 2017

    Enabling Robots to Communicate their Objectives
    Sandy H. Huang, David Held, Pieter Abbeel, Anca D. Dragan
    Robotics: Science and Systems (RSS), 2017

    Probabilistically Safe Policy Transfer
    David Held, Zoe McCarthy, Michael Zhang, Fred Shentu, Pieter Abbeel
    International Conference on Robotics and Automation (ICRA), 2017

  • 2016
  • Learning to Track at 100 FPS with Deep Regression Networks
    David Held, Sebastian Thrun, Silvio Savarese
    European Conference on Computer Vision (ECCV), 2016

    A Probabilistic Framework for Real-time 3D Segmentation using Spatial, Temporal, and Semantic Cues
    David Held, Devin Guillory, Brice Rebsamen, Sebastian Thrun, Silvio Savarese
    Robotics: Science and Systems (RSS), 2016
    [Project Page]

    Robust Single-View Instance Recognition
    David Held, Sebastian Thrun, Silvio Savarese
    International Conference on Robotics and Automation (ICRA), 2016
    Robust Real-Time Tracking Combining 3D Shape, Color, and Motion
    David Held, Jesse Levinson, Sebastian Thrun, Silvio Savarese
    International Journal of Robotics Research (IJRR), 2016

  • 2014
  • Combining 3D Shape, Color, and Motion for Robust Anytime Tracking
    Robotics: Science and Systems (RSS), 2014
  • 2013
  • Precision Tracking with Sparse 3D and Dense Color 2D Data - Best Vision Paper Finalist
    International Conference on Robotics and Automation (ICRA), 2013
  • 2012
  • A Probabilistic Framework for Car Detection in Images using Context and Scale
    International Conference on Robotics and Automation (ICRA), 2012
  • Older Work
  • Characterizing Stiffness of Multi-Segment Flexible Arm Movements
    David Held, Yoram Yekutieli, Tamar Flash
    International Conference on Robotics and Automation (ICRA), 2012
    Towards fully autonomous driving: Systems and algorithms
    Jesse Levinson, Jake Askeland, Jan Becker, Jennifer Dolson, David Held, Soeren Kammel, J. Zico Kolter, Dirk Langer, Oliver Pink, Vaughan Pratt, Michael Sokolsky, Ganymed Stanek, David Stavens, Alex Teichman, Moritz Werling, and Sebastian Thrun
    Intelligent Vehicles Symposium (IV), 2011.
    MVWT-II: The Second Generation Caltech Multi-Vehicle Wireless Testbed
    Zhipu Jinh, Stephen Waydo, Elisabeth B. Wildanger, Michael Lammers, Hans Scholze, Peter Foley, David Held, Richard M. Murray
    American Control Conference (ACC), 2004
    Surface waves and spatial localization in vibrotactile displays
    Haptics Symposium, 2010
    Characterization of Tactors Used in Vibrotactile Displays
    Journal of Computing and Information Science in Engineering, 2008