Tony Dear

About

Tony Dear is a roboticist whose research centers on the geometric motion planning and control of underactuated and non-holonomic systems, with a particular focus on snake robots and bio-inspired locomotion. His major contributions include developing novel control frameworks for multi-link snake robots with passive joints, demonstrating that effective locomotion can be achieved with fewer inputs than degrees of freedom—a counterintuitive insight with practical implications for simpler, more robust robot designs. He has also advanced the understanding of low-Reynolds-number swimming by extending Purcell’s classic three-link swimmer into three dimensions with yaw-pitch joint movements, and has explored optimal control strategies for aggressive aerial reorientation in robot divers. His work on deep reinforcement learning for snake robot locomotion (2020, 10 citations) and his "compound-wave control template" (2015, 9 citations) bridge robotics and biology, offering principled methods for discovering efficient gaits. With over 90 total citations across his top papers, Dear’s research is notable for its theoretical rigor—drawing on geometric mechanics and principal bundles—and its practical impact on designing simpler, more capable robots for challenging environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Locomotion of a multi-link non-holonomic snake robot with passive joints

   26 citations · 2020
2. 2
   Geometric Motion Planning for a Three-Link Swimmer in a Three-Dimensional low Reynolds-Number Regime

   14 citations · 2018
3. 3
   Optimal control for geometric motion planning of a robot diver

   11 citations · 2016
4. 4
   Locomotive analysis of a single-input three-link snake robot

   10 citations · 2016
5. 5
   Deep Reinforcement Learning for Snake Robot Locomotion

   10 citations · 2020
6. 6
   Robot-inspired biology: The compound-wave control template

   9 citations · 2015
7. 7
   Locomotion of a Multi-Link Nonholonomic Snake Robot

   4 citations · 2017
8. 8
   Nonlinear dimensionality reduction for kinematic cartography with an application toward robotic locomotion

   3 citations · 2014
9. 9
   Variations on the Role of Principal Connections in Robotic Locomotion

   2 citations · 2016
10. 10
    Guided Deep Reinforcement Learning for Articulated Swimming Robots

    2 citations · 2023

Key Collaborators

Contact & Links