Peter Will

About

Peter Will is a pioneering researcher whose career spans an remarkable breadth of robotics and computer vision, with his most transformative contributions centered on modular self-reconfigurable robotic systems. Best known for his foundational work on the CONRO project, Will helped establish the theoretical and engineering principles behind deployable robots capable of dynamically reshaping themselves into configurations such as snakes and hexapods. His landmark papers on CONRO modules and metamorphic robots have collectively amassed hundreds of citations, with his 2002 work on hormone-inspired adaptive communication alone attracting over 300 citations. A hallmark of Will's approach is the elegant application of biological metaphors to engineering challenges. By drawing on hormonal signaling models, he developed distributed control frameworks enabling robot swarms to self-organize without centralized direction — work that proved influential far beyond robotics, extending into computational modeling of biological pattern formation, including feather array morphogenesis. His early 1971 contribution on grid coding for machine vision demonstrates a career-long commitment to foundational advances. Will has also explored frontiers in nanorobotic assembly, reflecting an enduring curiosity about how intelligent structure and control can be achieved at every scale. His body of work has profoundly shaped how researchers think about autonomous, adaptive, and self-organizing systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Hormone-inspired adaptive communication and distributed control for CONRO self-reconfigurable robots

   305 citations · 2002
2. 2
   The Conro modules for reconfigurable robots

   224 citations · 2002
3. 3
   CONRO: Towards Deployable Robots with Inter-Robots Metamorphic Capabilities

   207 citations · 2000
4. 4
   Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms

   197 citations · 2004
5. 5
   Grid coding: A preprocessing technique for robot and machine vision

   142 citations · 1971
6. 6
   Integument pattern formation involves genetic and epigenetic controls: feather arrays simulated by digital hormone models.

   129 citations · 2004
7. 7
   Using role-based control to produce locomotion in chain-type self-reconfigurable robots

   85 citations · 2002
8. 8
   Docking among independent and autonomous CONRO self-reconfigurable robots

   84 citations · 2004
9. 9
   Hormone-controlled metamorphic robots

   81 citations · 2002
10. 10
    Nanorobotic assembly of two-dimensional structures

    74 citations · 2002

Key Collaborators

Contact & Links