About

J. Edward Colgate is the Walter P. Murphy Professor of Mechanical Engineering at Northwestern University, where he also serves as the Director of the NSF Engineering Research Center on Human Augmentation via Dexterity (HAND). His principal research interests include human-robot interaction, haptics, surface haptics, perception, and collaborative robots known as cobots, which he co-invented with Michael Peshkin. Professor Colgate has developed technologies that enhance touch-based interfaces such as touch screens and trackpads, and his current work focuses on tactile texture recording and reproduction, high-speed electroadhesive clutches for haptics and robotics, and robotic manipulation with dexterous hands. He has made significant contributions to the field of haptics and human-machine interaction through innovative research and numerous awards, including membership in the National Academy of Engineering and the National Academy of Inventors. Colgate's educational contributions include teaching courses on dynamic systems and robot design, as well as co-developing the 'Design Thinking and Communication' program at Northwestern, and serving as founding co-director of the Segal Design Institute.

Research topics

• Computer Science
• Sociology
• Artificial Intelligence
• Engineering
• Psychology
• Materials science
• Physics
• Mechanical engineering
• Composite material
• Library science
• Art
• Engineering management
• Simulation
• Acoustics
• Management
• Multimedia
• Pedagogy
• Computer vision
• Visual arts
• Mathematics education
• Electrical engineering
• Embedded system
• World Wide Web
• Thermodynamics

Selected publications

• Dead Zone Bilateral Control for High Performance Robotic Teleoperation

  IEEE Robotics and Automation Letters · 2026-03-19

  articleSenior author

  Autonomous robot policies are commonly trained using demonstration data acquired via robotic teleoperation, a process which can be time-intensive and physically demanding for human operators. Bilateral control can speed up robotic teleoperation by allowing the operator to feel the forces experienced by the remote manipulator, but it can also transmit undesirable forces, such as friction and damping, back to the operator. Here, we present dead zone bilateral control, an improvement to the conventional position-position bilateral control scheme that prevents the transmission of unwanted forces during free-space motion. We implement our controller on a custom 2-degree-of-freedom teleoperation device and show that it reduces the energy required for free-space motion by approximately 50%. During a user study in which 16 participants performed a peg rolling task, our controller reduced completion times by an average of 25% compared to the standard position-position bilateral controller. The results suggest that dead zone bilateral control can expedite the collection of teleoperated task demonstrations, allowing researchers to gather larger datasets for training autonomous robot policies.

  PublisherDOI

• Camera-Based Closed-Loop Fingertip Deflection Guidance: Pilot Demonstrations in Target Acquisition and Object Retrieval

  2026-04-13

  articleOpen accessSenior author

  Many eyes-free guidance systems convey direction through symbolic cues that require interpretation, potentially increasing cognitive load and competing with critical sensory channels. This work builds on prior studies of fingertip deflection as a physically-grounded guidance cue previously demonstrated in controlled 1D and 2D settings using an instrumented testbed. Here, a camera is integrated onto the actuation ring to enable object-referenced, closed-loop guidance during free reach. Using ArUco targets, the system estimates target displacement in the camera frame and maps this error into continuous fingertip deflection cues in real time. Two pilot demonstrations illustrate feasibility: (1) a Fitts-style touchscreen target acquisition task with trajectory visualization, and (2) an object-retrieval task with video-based evidence of guided approach and grasp. These vignettes ground a forthcoming study with participants who are blind and vision-impaired, and invite discussion on how ring-mounted sensing can best support embodied, eyes-free guidance in everyday interactions.

  PublisherDOI

• Demonstrating Eyes-Free Object Retrieval via Fingertip Deflection Guidance Using the NURing

  2026-04-13

  articleOpen accessSenior author

  Many eyes-free guidance systems convey directional information through symbolic cues requiring interpretation, potentially increasing cognitive load and competing with existing sensory channels. We present an interactive demonstration of fingertip deflection as a continuous, physically-intuitive guidance cue that gently biases the arm during reach. Motivated by a challenge frequently reported by individuals with blindness — that of safely retrieving small objects within arm’s reach — we developed an eyes-free, object-retrieval task. Building on our prior NURing, we extend fingertip deflection beyond instrumented testbeds by integrating a camera on the actuation ring. This allows the NURing to detect ArUco-tagged objects, estimate their pose relative to the camera, and drive continuous closed-loop deflection cues toward the target in real time. This demonstration invites attendees to experience this embodied guidance firsthand and to explore how fingertip deflection could support future assistive and collaborative systems that guide action through physical intuition rather than cognitive translation.

  PublisherDOI

• Full-field skin mechanics as a missing experimental layer in spatiotemporal haptics

  APL Engineering Physics · 2026-03-01

  articleOpen access

  Spatiotemporal haptic devices can now deliver distributed, multimodal touch across the body; however, their evaluation still relies predominantly on pointwise metrics and subjective user ratings. A critical experimental layer remains missing: full-field skin mechanics, which can systematically connect device actuation to perceptual outcomes.

  PublisherOA PDFDOI

• 3D Cal: An Open-Source Software Library for Depth Reconstruction on Vision-Based Tactile Sensors

  IEEE Robotics and Automation Letters · 2026-03-13

  article

  Tactile sensing plays a key role in enabling dexterous and reliable robotic manipulation, but realizing this capability requires substantial calibration to convert raw sensor readings into physically meaningful quantities. Despite its near-universal necessity, the calibration process remains ad hoc and labor-intensive. Here, we introduce 3D Cal, an open-source library that transforms a low-cost 3D printer into an automated probing device capable of generating large volumes of labeled training data for calibrating vision-based tactile sensors. 3D Cal also provides an end-to-end, user-friendly pipeline for training custom convolutional networks to produce high-quality depth reconstructions. Using 3D Cal, we systematically explore the relationship between training data volume and spatial reconstruction performance on two commercially available sensors, DIGIT and GelSight Mini, and derive practical, empirically-grounded guidelines for calibrating these sensors. Finally, we demonstrate depth reconstruction performance on the DIGIT and GelSight Mini comparable to state-of-the-art methods, achieving average reconstruction errors of 156 μm and 205 μm on unseen objects, respectively. By automating tactile sensor calibration, 3D Cal can accelerate tactile sensing research, simplify sensor deployment, and facilitate the integration of tactile sensing in robotic platforms.

  PublisherDOI

• Strong yet backdrivable robots through capstan-amplified electroadhesive clutches

  npj Robotics · 2026-03-30

  articleOpen accessSenior author

  Dexterous manipulation in compact robots requires combining high force output with passive backdrivability; capabilities that conventional geared actuation struggles to deliver. We introduce an electromechanical multiplexing architecture that routes power from a single drive shaft to multiple outputs and mechanically grounds them using capstan-amplified electroadhesive (EA) clutches in a load-transfer configuration. Wrapping thin-film EA clutches on cylindrical counter-surfaces provides exponential gain for EA braking force, while voltage pulse-width modulation yields sub-newton (<0.1 N) force resolution and low reflected impedance from the drive shaft, enabling compliant interaction. Force transmission behavior is explained by a mechanics-based model of curved clutches and supported by strain imaging showing a propagating, load-carrying slip front. Switching measurements under bipolar high-voltage drive demonstrate millisecond-scale release and effective operation near 1 kHz, enabling high-rate force modulation. Leveraging this clutch-level understanding, we realized a well-behaved system: a tendon-driven, two-finger gripper that transitions between highly backdrivable, cooperative grasping and firm, energy-efficient holding. By decoupling pulling from latching, the load-transfer design mechanically grounds the output without sustained motor torque, outlining a scalable route to compact, low-power robotic hands that maintain backdrivability while spanning three orders of magnitude in force.

  PublisherOA PDFDOI

• Twenty Years of World Haptics: Retrospective and Future Directions

  IEEE Transactions on Haptics · 2025-07-01

  article1st authorCorresponding
  PublisherDOI

• NURing: A Tendon-Driven Wearable Ring for On-Demand Kinesthetic Haptic Feedback

  2025-07-08 · 2 citations

  articleSenior author

  Generating salient and intuitively understood haptic feedback on the human finger through a non-intrusive wearable remains a challenge in haptic device development. Most existing solutions either restrict the hand and finger's natural range of motion or impede sensory perception, quickly becoming intrusive during dexterous manipulation tasks. Here, we introduce NURing (Non-intrUsive Ring), a tendon-actuated haptic device that provides kinesthetic feedback by deflecting the finger. The NURing is easily donned and doffed, enabling on-demand kinesthetic feedback while leaving the hand and fingers free for dexterous tasks. We demonstrate that the device delivers perceptually salient feedback and evaluate its performance through a series of uniaxial motion guidance tasks. The lightweight NURing device, measuring approximately 220 g, can generate guidance cues at up to 1 Hz, enabling participants to identify target directions in under 3s with a 1.5° steady-state error, corresponding to a fingertip deviation of less than 11mm. Additionally, it can guide users along complex, smooth trajectories with an average trajectory error of 7°. These findings highlight the effectiveness of fingertip deflection as a kinesthetic feedback modality, enabling precise guidance for real-world applications such as sightless touchscreen navigation, assistive technology, and both industrial and consumer augmented/virtual reality systems.

  PublisherDOI

• Full freedom-of-motion actuators as advanced haptic interfaces

  Science · 2025-03-27 · 59 citations

  article

  The sense of touch conveys critical environmental information, facilitating object recognition, manipulation, and social interaction, and can be engineered through haptic actuators that stimulate cutaneous receptors. An unfulfilled challenge lies in haptic interface technologies that can engage all the various mechanoreceptors in a programmable, spatiotemporal fashion across large areas of the body. Here, we introduce a small-scale actuator technology that can impart omnidirectional, superimposable, dynamic forces to the surface of skin, as the basis for stimulating individual classes of mechanoreceptors or selected combinations of them. High-bit haptic information transfer and realistic virtual tactile sensations are possible, as illustrated through human subject perception studies in extended reality applications that include advanced hand navigation, realistic texture reproduction, and sensory substitution for music perception.

  PublisherDOI

• High-performance electroadhesive clutches with multilayered architecture

  Science Advances · 2025-02-14 · 3 citations

  articleOpen accessSenior authorCorresponding

  Electroadhesive (EA) clutches are promising for advanced motion and force control in robotics, haptics, and rehabilitation, owing to their compactness and light weight. However, their practical use is limited by the inability to deliver high forces at low voltages, primarily due to a lack of understanding of their mechanics. We introduce a novel deformable body fracture mechanics approach and high-resolution strain field imaging to reveal that nonuniform stress distributions cause EA clutches to fail through delamination and crack propagation. Using this insight, we developed EA clutches sustaining 22 newtons over 1 square centimeter at 100 volts, achieving the highest stress per voltage among similar clutches. This was achieved by incorporating a soft interlayer and peeling stopper for uniform stress distribution and mitigating the failure modes. These EA clutches were integrated into a lightweight ring-based wearable system for finger rehabilitation and haptics. Our findings lay the groundwork for designing low-voltage, high-performance EA clutches for next-generation motion and force control applications.

  PublisherDOI

Recent grants

• HCC: Medium: Collaborative Research: Force Feedback for Fingertips

  NSF · $808k · 2013–2017

• Collaborative Research: HCC: Medium: TouchBots for Surface Haptics

  NSF · $800k · 2021–2025

• Variable Compliance Haptic Field Displays

  NSF · $529k · 2004–2008

• HCC: Medium: Collaborative Research: Surface Haptics via Tractive Forces

  NSF · $995k · 2010–2015

• EAGER: Preliminary Investigation of Virtual Tactual Stereognosis

  NSF · $154k · 2009–2011

Frequent coauthors

• Michael A. Peshkin

  Northwestern University

  129 shared

• Roberta L. Klatzky

  Carnegie Mellon University

  23 shared

• Eric L. Faulring

  Hunter Defense Technologies (United States)

  15 shared

• Michaël Wiertlewski

  Delft University of Technology

  15 shared

• Gregory B. Olson

  Massachusetts Institute of Technology

  11 shared

• Ann McKenna

  University of Iowa

  10 shared

• Craig Shultz

  University of Illinois Urbana-Champaign

  10 shared

• Kevin Lynch

  10 shared

Education

• PhD, Mechanical Engineering

  Massachusetts Institute of Technology

  1988

Awards & honors

• Member, National Academy of Engineering since 2021
• IEEE Transactions on Haptics Best Application Paper Award fo…
• Researcher to Know, 2018 (Inaugural Class)
• Illinois Science & Technology Coalition Best Paper Award, 20…
• Fellow, National Academy of Inventors since 2015