Papers
6
Total Citations
91
H-Index
4
About
Matthew Hale is a leading researcher in the intersection of robotics, control theory, and privacy, with key contributions in legged locomotion, multi-agent optimization, and privacy-preserving control. His early work on autonomous legged hill and stairwell ascent (48 citations) pioneered perceptually-triggered locomotion primitives that enabled rugged robots to negotiate unstructured climbing terrain—a foundational achievement in field robotics. In multi-agent systems, Hale developed decentralized algorithms for classic combinatorial problems like weapon–target assignment (13 citations) and cloud-based optimization under communication delays (3 citations), advancing scalable coordination for distributed teams. More recently, he has been at the forefront of privacy-aware control, introducing differentially private controller synthesis for metric temporal logic specifications (13 citations) and trust-driven privacy in human-robot interactions (4 citations). His Hamiltonian-based algorithm for optimal control (10 citations) offers a novel computational approach for problems with easy-to-compute Hamiltonian minimizers. With a growing portfolio spanning from autonomous climbing to privacy in multi-agent systems, Hale’s work has significant implications for secure, cooperative robotics and distributed intelligence.
Research Focus
Key Achievements
Top Papers
- 1Autonomous legged hill and stairwell ascent48 citations · 2011
- 2Decentralized Weapon–Target Assignment Under Asynchronous Communications13 citations · 2022
- 3
- 4Hamiltonian-Based Algorithm for Optimal Control10 citations · 2016
- 5Trust-Driven Privacy in Human-Robot Interactions4 citations · 2019
- 6