Robotic Human Augmentation

2017 ~ Present | Department of Defense, NSF, CHOA, FDA, GTRI, Lockheed Martin, Rubicon, Nextlex, Vicon


The EPIC (Exoskeleton and Prosthesis Intelligent Controls) Lab research areas include automation & mechatronics and bioengineering with a focus on the control of powered robotic prostheses and exoskeletons to assist human movement. We implement biological signal processing, intent recognition, and control systems based on EMG and mechanical sensors to improve human-machine capabilities. Our primary goal is to use robotic augmentation technology to restore human movement to individuals with mobility disability.

Issues Involved or Addressed

Lab research is divided into multidisciplinary research subteams of graduate and undergraduate students. Each team is led by at least one graduate student mentor who directs the team to assist in their primary research goals. To best ensure meaningful contributions, each VIP member is selected for a specific subteam. As an applicant, we ask that you first review the subteams (listed below) and reach out to the contacts corresponding to your project(s) of interest. Successfully contacting the graduate student mentors before applying to VIP greatly helps us in determining who to accept! When you submit your application, please indicate the subteam of interest in the comment box.


Description: This team will primarily focus on developing the testing platform for a hip exoskeleton to explore if this system can assist a user to evade moving threats. We tackle this through three key research areas. (1) Virtual Reality: our current experiment investigates the influence of AI on human performance in multiple threat scenarios to quantify the benefits of intelligent path planning combined with human intuition. (2) Intent recognition: to understand once we have alerted a person, how do we extract their intended movement through wearable sensor in tandem with sensor fusion and machine learning techniques. (3) Hip exoskeleton: we are currently assembling our exoskeleton and will need assistance getting the system online in a safe and controllable manner. Major short-term goals which undergraduate students can tackle include testing and hip exoskeleton assembly; exo UI programming and control, virtual environment experimentation; and system integration/processing for motion capture. We are seeking students who have interest and/or experience in Unity, C#, virtual reality, AI/machine learning, microprocessors/controllers, sensors, mechatronic design, controls. Some of the majors we will be recruiting for are ECE, CS, BME, and ME. Contact: Aakash


Description: This team is dedicated to developing and testing a novel exosuit for reducing lumbar loading during lifting tasks. Undergraduate involvment includes conducting human-subject experimentation and analysis and piloting exosuit controllers, mechatronics improvements, and machine learning-based human state estimators. Undergraduate students are expected to have previous experience in embedded programming, machine learning, mechanical design, mechatronics, and/or biomechanics. Previous experience in Python, C++, Matlab, and/or Solidworks is also encouraged. Contact: Dean Molinaro(


Description: This team aims to understand how humans maintain their balance in unsteady environments and how balance can be augmented using a robotic hip exoskeleton. The team uses our lab’s CAREN system, a treadmill mounted on a Stewart platform, to collect biomechanics information following rapid perturbations that cause a loss of balance. We are using this information to understand the biomechanics/neural control of agile balance recovery and predict disruptions to balance using machine learning. The team is also designing and testing a new 2-degree-of-freedom hip exoskeleton that will be used to study balance augmentation. Students on this team have the opportunity to assist in human subjects experiments, analysis of biomechanical and neural control data, development of machine learning models to predict loss of balance, mechanical assembly, design of exoskeleton interfaces, and testing of a robotic exoskeleton. Students who have or would like to gain experience in these areas would be a great fit for this team! Experience with Matlab and/or SolidWorks is preferred. Students from all disciplines are welcome to apply. Contact: Jennifer Leestma (


Description: This team works to understand the link between wearable robotics and the human biomechanical/metabolic system during walking. Using autonomous hip and ankle exoskeletons, we enhance walking (decreasing energy consumption or increasing casual walking speed) while collecting and analyzing motion capture data (kinematics and kinetics of motion), EMG signals (muscular activation), and respiratory data (energetic cost). We will apply this collected information to optimize controllers to drive specific changes to the musculoskeletal system in specific populations (able-bodied, older adults). All students will assist with data collection and analysis and a possible subgroup project. This semester’s project is focused on controller optimization using 2 exoskeletons simultaneously (hip and ankle) to increase casual walking speed in older adults. We are looking for team members experienced in MATLAB, with an independent work mindset, interested in physiological data collection and analysis with weekly deliverables. Contact: Ben Shafer (


Description: The goal of this project is to promote/enhance walking rehabilitation for children with walking disabilities. The team will be focusing on human-subject experiments to investigate the effect of gait assistance using a robotic knee exoskeleton. Additionally, the team will investigate developing techniques to understand walking environmental conditions using machine-learning approaches. The data collected from the experiment will include 3-D motion capture, muscle activity, device performance, and user feedback which facilitate biomechanical analysis. Students from BME, ME, and ECE backgrounds are all suitable for the project. Additionally, students with skills and knowledge in machining, LabVIEW, Matlab, circuit design, or biomechanics will be helpful. Contact: Dawit Lee (


Description: This team will be dedicated to develop and validate different control strategies on a knee and ankle prosthetic device. The main goal involves creating a user independent system to allow individuals with transfemoral amputation to improve their ambulation in common community tasks. We will be collecting data from different sensors which included embedded mechanical sensors (IMU’s and encoders), EMG signals, motion capture, and metabolics on a variety of terrain. Skills that will be looked but not limited to include strong programming skills (C++, Python, Qt, & ROS) and mechatronics (PCB design, controls, and embedded systems design). Applicants are encouraged to become Invention Studio or HIVE PI’s. We are looking for highly motivated students specifically from ECE and CS. Contact: Jairo Maldonado (


Description: The robotic hip exoskeleton team primarily focuses on implementing intellectual controllers to understand the human intent using during locomotion. The team implements state-of-the-art machine learning algorithms to better understand what the exoskeleton user is trying to do (e.g., ambulation modes, walking speed) and provide natural and seamless assistance. Prospective students joining the team will learn how to utilize different on-board mechanical sensors from the exoskeleton to develop a machine learning model that can be deployed to a real-time robot. Additionally, students will be exposed to learn different engineering skills such as human biomechanics using motion capture system, muscle signal analysis, and measuring human energetic cost. Students will be exposed to an interdisciplinary aspect of the project, such as biomechanical analysis, modeling/simulation software, mechatronic design/fabrication of an actuator, and integrating artificial intelligence for controlling the exoskeleton. Contact: Inseung Kang (

Methods and Technologies

  • Control Systems
  • Mechatronics
  • Machine Learning
  • Signal Processing
  • Data Analysis
  • Embedded Programming
  • Human Subject Testing
  • Mechanical Design
  • Human Biomechanics
  • Machining
  • Wearable Robotics
  • Motion Capture
  • EMG
  • Metabolics
  • Instrumented Treadmill
  • Force Plates

Academic Majors of Interest

  • Computer Science
  • Biomedical Engineering
  • Computer Engineering
  • Electrical Engineering
  • Mechanical Engineering
  • Robotics

Preferred Interests and Preparation

As the research is an interdisciplinary research, wide range of skills and experiences will be highly applicable to the study. Some of the core skills will include mechanical design, machining, mechatronics, embedded and MATLAB programming, and signal processing. Below are examples of specific skills that would be related to each major.

ME - Background/interest in mechanical design and machining for fast prototyping. Training in Montgomery machining mall for CNC, mill, lathe, 3D printer is recommended.

EE/CompE - Background/interest in mechatronics and electrical circuitry design. Knowledge in circuit design and signal processing is recommended

CS - Background/interest in software development for embedded programming. Knowledge/interest in language (C, Labview, MATLAB, python) is recommended.

BME - Background/interest in clinical testing for device evaluation. Knowledge/interest in human biomechanics and design of biomedical robotic devices/controllers as human and biological data processing is recommended.

Meeting Schedule & Location

10:55-11:45 (SP22); 11:00-11:50 (FA22)
Meeting Location 
Love 109
Meeting Day 

Team Advisors

Dr. Aaron Young
  • Mechanical Engineering

Partner(s) and Sponsor(s)

Department of Defense, NSF, CHOA, FDA, GTRI, Lockheed Martin, Rubicon, Nextlex, Vicon

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