About

Kevin Lynch is a Professor of Mechanical Engineering at Northwestern University and the Director of the Center for Robotics and Biosystems. His research interests encompass robotic manipulation, robot locomotion, physical human-robot interaction, and distributed control of robot swarms. Throughout his career, he has contributed significantly to the fields of robotics and automation, earning numerous awards including the 2022 George Saridis Leadership Award in Robotics and Automation, the Harashima Award for Innovative Technologies in 2017, and the IEEE Fellow recognition in 2010. He has also been honored with the Charles Deering McCormick Professor of Teaching Excellence from 2007 to 2010, and the NSF Career Award in 1998. His work has advanced understanding in robotic systems, human-robot collaboration, and control strategies, making impactful contributions to both academic research and practical applications in engineering.

Research topics

• Computer Science
• Artificial Intelligence
• Human–computer interaction
• Computer network
• Distributed computing
• Mathematics
• Algorithm
• Telecommunications
• Theoretical computer science
• Engineering
• Discrete mathematics

Selected publications

• Simplifying rehabilitation control of lower-limb exoskeletons in five ambulation modes via dataset-driven state-machine calibration

  Journal of NeuroEngineering and Rehabilitation · 2026-03-20

  articleOpen access

  Lower-limb exoskeletons are a useful tool in rehabilitation settings as they can provide customized assistance to individuals during functional exercises. These approaches typically rely on state-machine-based control with impedance controllers tailored to different locomotion phases, ensuring appropriate assistance across various activities and environments. However, these methods necessitate lengthy calibration procedures, as many impedance parameters need to be fine-tuned to provide appropriate assistance for various activities (e.g., overground walking, ramps, and stairs). This study presents three contributions: (1) a state-machine-based control strategy for partial assistance lower-limb exoskeletons, (2) a computational method to extract reference trajectories from a benchmark dataset (Camargo et al. in J Biomech 119:110320, 2021), enabling the identification of state-machine controller parameters and simplifying calibration procedures and (3) a dataset of 19 healthy individuals walking in five walking conditions (overground walking, upstairs, downstairs, up ramps, and down ramps) using either the state-machine approach or a transparent controller. The state-machine controller produced in average more negative interaction power ($$-2.6\times 10^{-2}$$ W/kg) compared to transparent control ($$0.8\times 10^{-2}$$ W/kg), indicating greater user assistance. Preferred walking speed was notably faster with the state-machine controller, particularly on level ground, ramps and stairs ascent (25–32% increase). Kinematic analysis revealed closer alignment to able-bodied gait patterns with the state-machine controller, suggesting improved gait quality. At the same time, the dataset of the collected locomotion activities (dataset link) will constitute a new benchmark dataset for locomotion. In this work, we presented and evaluated a novel state-machine-based control strategy for partial-assistance lower-limb exoskeletons. In this approach, reference trajectories are extracted from a benchmark dataset, simplifying calibration procedures. Additionally, we provide a dataset of 19 healthy individuals using two exoskeleton controllers. The proposed controller will be applied to patient populations, while the dataset will serve as a valuable resource for advancing robust and effective control mechanisms through machine learning techniques.

  PublisherDOI

• Cooperative Payload Estimation by a Team of Mocobots

  ArXiv.org · 2025-02-07

  preprintOpen accessSenior author

  For high-performance autonomous manipulation of a payload by a mobile manipulator team, or for collaborative manipulation with the human, robots should be able to discover where other robots are attached to the payload, as well as the payload's mass and inertial properties. In this paper, we describe a method for the robots to autonomously discover this information. The robots cooperatively manipulate the payload, and the twist, twist derivative, and wrench data at their grasp frames are used to estimate the transformation matrices between the grasp frames, the location of the payload's center of mass, and the payload's inertia matrix. The method is validated experimentally with a team of three mobile cobots, or mocobots.

  PublisherOA PDFDOI

• Effects of Uni- and Bi-Directional Interaction During Dyadic Ankle and Wrist Tracking

  IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2025-01-01 · 2 citations

  articleOpen access

  Haptic human-robot-human interaction allows users to feel and respond to one another's forces while interfacing with separate robotic devices, providing customizable infrastructure for studying physical interaction during motor tasks (e.g., physical rehabilitation). For upper- and lower-limb tracking tasks, previous work has shown that virtual interactions with a partner can improve motor performance depending on the skill level of each partner. However, whether the mechanism explaining these improvements is identical in the upper and lower limbs is an open question. In this work, we investigate the effects of haptic interaction between healthy individuals during a trajectory tracking task involving single-joint movements at the wrist and ankle. We compare tracking performance and muscle activation during haptic conditions where pairs of participants were uni- and bidirectionally connected to investigate the contribution of real-time responses from a partner during the interaction. Findings showed similar improvements in tracking performance during bidirectional interaction for both the wrist and ankle. This was observed despite distinct strategies in muscle co-contraction between joints, as co-contraction was dependent on partner ability for the wrist but not the ankle. For each joint, bidirectional and unidirectional interaction resulted in similar improvements for the worse partner in the dyad. For the better partner, bidirectional interaction resulted in greater improvements than unidirectional interaction. While these results suggest that unidirectional interaction is sufficient for error correction of less skilled individuals during simple motor tasks, they also highlight the mutual benefits of bidirectional interaction which are consistent across the upper and lower limbs.

  PublisherDOI

• Cooperative Payload Estimation by a Team of Mocobots

  IEEE Robotics and Automation Letters · 2025-08-11 · 1 citations

  articleSenior author

  For high-performance autonomous manipulation of a payload by a mobile manipulator team, or for collaborative manipulation with the human, robots should be able to discover where other robots are attached to the payload, as well as the payload's mass and inertial properties. In this paper, we describe a method for the robots to autonomously discover this information. The robots cooperatively manipulate the payload, and the twist, twist derivative, and wrench data at their grasp frames are used to estimate the transformation matrices between the grasp frames, the location of the payload's center of mass, and the payload's inertia matrix. The method is validated experimentally with a team of three mobile cobots, or mocobots.

  PublisherDOI

• Therapist-Exoskeleton-Patient Interaction for Gait Therapy

  ArXiv.org · 2025-07-21

  preprintOpen access

  Following a stroke, individuals often experience mobility and balance impairments due to lower-limb weakness and loss of independent joint control. Gait recovery is a key goal of rehabilitation, traditionally achieved through high-intensity therapist-led training. However, manual assistance can be physically demanding and limits the therapist's ability to interact with multiple joints simultaneously. Robotic exoskeletons offer multi-joint support, reduce therapist strain, and provide objective feedback, but current control strategies often limit therapist involvement and adaptability. We present a novel gait rehabilitation paradigm based on physical Human-Robot-Human Interaction (pHRHI), where both the therapist and the post-stroke individual wear lower-limb exoskeletons virtually connected at the hips and knees via spring-damper elements. This enables bidirectional interaction, allowing the therapist to guide movement and receive haptic feedback. In a study with eight chronic stroke patients, pHRHI training outperformed conventional therapist-guided treadmill walking, leading to increased joint range of motion, step metrics, muscle activation, and motivation. These results highlight pHRHI's potential to combine robotic precision with therapist intuition for improved rehabilitation outcomes.

  PublisherOA PDFDOI

• A Benchmark Dataset for Lower-Limb Exoskeletons Assisting Five Ambulation Modes

  Research Square · 2025-03-03

  preprintOpen access
  PublisherOA PDFDOI

• A New Look at Civic Design

  Journal of Architectural Education · 2025-07-03

  article1st authorCorresponding
  PublisherDOI

• Exoskeleton-Mediated Physical Teacher-Student Interaction for Gait Training: A Pilot Study

  Biosystems & biorobotics · 2025-01-01

  book-chapter
  PublisherDOI

• Effects of Uni- and Bi-directional Interaction During Dyadic Ankle and Wrist Tracking

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-11-28 · 1 citations

  preprintOpen access

  Abstract Haptic human-robot-human interaction allows users to feel and respond to one another’s forces while interfacing with separate robotic devices, providing customizable infrastructure for studying physical interaction during motor tasks (i.e., physical rehabilitation). For both upper- and lower-limb tasks, previous work has shown that virtual interactions with a partner can improve motor performance and enhance individual learning. However, whether the mechanism of these improvements generalizes across different human systems is an open question. In this work, we investigate the effects of haptic interaction between healthy individuals during a trajectory tracking task involving single-joint movements at the wrist and ankle. We compare tracking performance and muscle activation during haptic conditions where pairs of participants were uni- and bidirectionally connected, in order to investigate the contribution of real-time responses from a partner during the interaction. Findings indicate similar improvements in tracking performance during the bidirectional interaction for both the wrist and ankle, despite significant differences in how individuals modulated co-contraction. For each joint, bidirectional and unidirectional interaction resulted in similar improvements for the worse partner in the dyad. For the better partner, bidirectional interaction outperformed unidirectional interaction, likely due to changes in movement planning that were not observed in the unidirectional condition. While these results suggest that unidirectional interaction is sufficient for error correction of less skilled individuals during simple motor tasks, they also highlight the mutual benefits of bidirectional interaction which are consistent across the upper and lower limbs.

  PublisherOA PDFDOI

• Deep-Learning Estimation of Weight Distribution Using Joint Kinematics for Lower-Limb Exoskeleton Control

  IEEE Transactions on Medical Robotics and Bionics · 2024-11-21 · 5 citations

  article

  In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be used in the exoskeleton’s controller to compensate the dynamics of the exoskeleton and to assign impedance parameters. Typically the weight distribution is calculated using data from sensors such as treadmill force plates or insole force sensors. However, these solutions increase both the setup complexity and cost. For this reason, we propose a deep-learning approach that uses a short time window of joint kinematics to predict the weight distribution of an exoskeleton in real time. The model was trained on treadmill walking data from six users wearing a four-degree-of-freedom exoskeleton and tested in real time on three different users wearing the same device. This test set includes two users not present in the training set to demonstrate the model’s ability to generalize across individuals. Results show that the proposed method is able to fit the actual weight distribution with <inline-formula> <tex-math notation="LaTeX">$R^{2}=0.9$ </tex-math></inline-formula> and is suitable for real-time control with prediction times less than 1 ms. Experiments in closed-loop exoskeleton control show that deep-learning-based weight distribution estimation can be used to replace force sensors in overground and treadmill walking.

  PublisherDOI

Recent grants

• Collaborative Research: Kinematic Reductions for Underactuated Mechanical Systems

  NSF · $201k · 2003–2007

• RI: Small: Dynamic In-Hand Robotic Manipulation

  NSF · $415k · 2015–2019

• Programmable Vibratory Force Fields for Parts Handling

  NSF · $278k · 2007–2011

• RI: Medium: Planning and Control for Dynamic Robotic Manipulation

  NSF · $854k · 2010–2016

Frequent coauthors

• Randy A. Freeman

  Northwestern University

  51 shared

• José L. Pons

  Shirley Ryan AbilityLab

  50 shared

• Emek Barış Küçüktabak

  Shirley Ryan AbilityLab

  47 shared

• Matthew L. Elwin

  Northwestern University

  46 shared

• Eric J. Perreault

  Shirley Ryan AbilityLab

  38 shared

• Matthew R. Short

  37 shared

• Yue Wen

  Beijing Institute of Technology

  35 shared

• Levi J. Hargrove

  Northwestern University

  33 shared

Labs

• Center for Robotics and BiosystemsPI

Awards & honors

• George Saridis Leadership Award in Robotics and Automation (…
• Harashima Award for Innovative Technologies (2017)
• IEEE Fellow (2010)
• Charles Deering McCormick Professor of Teaching Excellence (…
• Society of Automotive Engineers Ralph R. Teetor Educational…