Matthew Antone

About

Matthew Antone is a leading figure in field robotics, whose work bridges the gap between autonomous systems and real-world, unstructured environments. His research centers on whole-body planning, state estimation, and human-robot interaction, with a particular focus on enabling humanoid robots and heavy machinery to operate reliably alongside people. Antone's most impactful contributions include an architecture for online affordance-based perception and whole-body planning (140 citations), which was critical to MIT's performance in the DARPA Robotics Challenge. He also developed a drift-free state estimation algorithm for humanoid robots that fuses kinematic, inertial, and LIDAR data (96 citations), and pioneered continuous locomotion over uneven terrain using only passive stereo vision (75 citations). Beyond humanoids, Antone led the creation of a voice-commandable robotic forklift capable of working safely in minimally-prepared outdoor environments (66 citations), demonstrating a practical vision for collaborative autonomy. His work on the high-rate, heterogeneous data set from the DARPA Urban Challenge (68 citations) remains a valuable resource for autonomous driving research. Through these achievements, Antone has advanced the frontier of robots that can perceive, plan, and act in the complex, dynamic world we inhabit.

Research Focus

Key Achievements

Top Papers

1. 1
   An Architecture for Online Affordance‐based Perception and Whole‐body Planning

   140 citations · 2014
2. 2
   Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

   96 citations · 2014
3. 3
   Continuous humanoid locomotion over uneven terrain using stereo fusion

   75 citations · 2015
4. 4
   A High-rate, Heterogeneous Data Set From The DARPA Urban Challenge

   68 citations · 2010
5. 5
   A voice-commandable robotic forklift working alongside humans in minimally-prepared outdoor environments

   66 citations · 2010
6. 6
   A Situationally Aware Voice‐commandable Robotic Forklift Working Alongside People in Unstructured Outdoor Environments

   47 citations · 2014
7. 7
   A summary of team MIT's approach to the virtual robotics challenge

   19 citations · 2014
8. 8
   One-shot visual appearance learning for mobile manipulation

   10 citations · 2012
9. 9
   Vision-Based Reacquisition for Task-Level Control

   3 citations · 2013
10. 10
    Appearance-based object reacquisition for mobile manipulation

    3 citations · 2010

Key Collaborators

Contact & Links