David A. Handelman

About

David A. Handelman is a pioneering researcher whose career spans the intersection of robotics, artificial intelligence, and human-machine interaction. His foundational work in the late 1980s and early 1990s established a compelling framework for integrating neural networks with knowledge-based systems for robotic control, drawing inspiration from human motor skill acquisition—an approach that earned his 1990 paper over 120 citations and influenced a generation of intelligent robotics research. Handelman has consistently pursued the ambitious question of whether robots can learn as humans do, developing biologically inspired methodologies that bridge symbolic and subsymbolic AI paradigms. His impact extends into the modern era, with significant contributions to brain-machine interfaces enabling bimanual robotic limb control for individuals with sensorimotor deficits, accumulating nearly 60 citations since 2022. More recently, his work has expanded into human-robot teaming and the ethical dimensions of autonomous systems, reflecting a thoughtful awareness of the societal responsibilities accompanying advanced robotics. From early explorations of robot choreography to cutting-edge adaptive teaming architectures, Handelman's body of work demonstrates a rare combination of technical depth and humanistic vision, making him a distinctive and enduring voice in intelligent robotics research.

Research Focus

Key Achievements

Top Papers

1. 1
   Integrating neural networks and knowledge-based systems for intelligent robotic control

   121 citations · 1990
2. 2
   Shared Control of Bimanual Robotic Limbs With a Brain-Machine Interface for Self-Feeding

   59 citations · 2022
3. 3
   Integrating knowledge-based system and neural network techniques for robotic skill acquisition

   17 citations · 1989
4. 4
   Designing robots that do no harm: understanding the challenges of Ethics for Robots

   15 citations · 2023
5. 5
   Robot choreography: An artistic-scientific connection

   10 citations · 1996
6. 6
   Integrating neural networks and knowledge-based systems for robotic control

   9 citations · 2003
7. 7
   Robotic Skill Acquisition Based on Biological Principles

   8 citations · 2020
8. 8
   Fast Sensorimotor Skill Acquisition based on Rule-Based Training of Neural Networks

   8 citations · 1993
9. 9
   Adaptive human-robot teaming through integrated symbolic and subsymbolic artificial intelligence: preliminary results

   7 citations · 2022
10. 10
    <title>Can robots learn like people do?</title>

    6 citations · 1990

Key Collaborators

Contact & Links