Human-Robot Teaming
Mentor: Dr. Julie A. Adams

This project seeks to develop new methods for adapting interaction between a human and a robot in a one-to-one partnership relationship based on the human's cognitive workload. The human dons various wearable sensors that provide a data stream that is analyzed to detect high and low workload conditions. Once a high or low workload condition is detected, the robot can adapt its interaction method with the human. REU students will work to develop new algorithms for detecting changes in workload and new algorithms for adapting the robot's interactive capabilities based on the workload detection. The project will require algorithm design, implementation and software testing. Further, REU students will evaluate the algorithms with actual human-robot teams.


Design of Implants for Attaching Muscle and Tendons to Improve Human Hand Function
Mentor: Dr. Ravi Balasubramanian

Current reconstructive orthopedic surgeries use sutures to attach muscles and tendons. However, this leads to poor surgical outcomes because of the suture’s limited ability to transmit the muscle’s forces and movement to the tendons. It is expected that using passive implants, such as pulleys and rods, to surgically construct mechanisms in situ using the existing biological tendons will significantly improve post-surgery function (when compared to using sutures) and lead to the development of new surgical procedures. 

Example projects:


Robot Grasping
Mentors: Dr. Cindy Grimm and Dr. Joe Davidson

Humans have no trouble picking up and manipulating objects, yet we've struggled to impart that ability to robotic manipulators. This is in part because robotic manipulators lack much of the sensory feedback human hands have, but it's also because we, as humans, are not very good about reasoning about what we do instinctively. The goal of this project is to develop tools and user studies that will let us capture that knowledge and apply it to robotic manipulation tasks. We apply these studies to several areas: fruit picking, object manipulation, and light industrial tasks.

Example projects:

  • Use reinforcement learning to improve grasping and manipulation of objects, particularly apples
  • Improve the mechanical and sensor design of a hand to improve manipulation skills
  • Analyze complex manipulation tasks to reduce them to simpler motion primitives


Robots for Health Promotion
Mentor: Dr. Naomi Fitter

Compared to other types of interactive technologies, robots possess a unique ability to motivate people because people tend to perceive them as a "social other," rather than a tool or device. The OSU SHARE Lab studies applications of robots in health-promoting scenarios, including ergonomics coaching and physical activity encouragement. For example, computer users face increased risks of heart disease, diabetes, and eyestrain due to prolonged periods of sitting and looking at a screen without taking a break; we are studying the ability of robots to help computer users take breaks, stand up, and move more during the workday. In another use case, children are becoming more sedentary over time; we are curious about the role robots can play in encouraging physical exploration and play.

Example projects:

  • Using reinforcement learning to improve robot prompting strategies
  • Designing and evaluating robot behaviors
  • Gathering and analyzing human user data


Geometry of Locomotion
Mentor: Dr. Ross Hatton

Many animals make full-body contact with the ground as they crawl, slither, or burrow. We're studying the geometry of the system's body motions to better understand how this process works, and using that knowledge to make robots that can take advantage of the underlying principles.

Example projects:

  • Construction of snake-like robots
  • Mathematical analysis of kinematics and dynamics


CHARISMA Lab Projects
Mentor: Dr. Heather Knight

The CHARISMA Lab, headed by Dr. Knight, is seeking REU students to support two projects:

1) The first project will help advance the state of the art in agricultural robotics by studying ways in which mechanical nut harvesters can aid human drivers and farmers. This project will involve formative research advancing human-in-the-loop agricultural systems, working closely with interdisciplinary teams in Washington, Oregon, and California. 2) The second project will focus on how expressive motion can influence the experience and efficacy of robots that help to feed humans. This project seeks to use socially inspired expressive motion pathways to inform the programming of a robot seeking to offer food to humans.

Helpful prerequisite skills include programming experience, interest in exploratory design research, and aiding in human user studies. Students will help develop technology, run human subjects experiments, and analyze data to help further service robot expressions in relation to service robot tasks.


Hybrid Motion Platform Design for Ultra-High-Speed Laser Cutting
Mentor: Dr. Burak Sencer

Hybrid-feed-drive motion platforms are well utilized in semiconductor manufacturing systems. They provide a large work area, high speed, and high accuracy. These motion platforms (positioning systems) are designed based on a redundant use of a coarse and a fine servo motor. The coarse axis (motor) provides large stroke and high power; whereas, the fine axis provides small stroke but very high acceleration. By combining both motors in a redundant mechanism, large stroke (work area), and at the same time high-speed positioning, can be achieved.

The objective of this project is to design a Cartesian hybrid redundant motion platform for ultra-high speed laser machining applications. The project is heavily mechatronics- and motion control-focused. Developed mechatronic systems and control algorithms will be demonstrated in real-time on the designed test setup.


    Multi-Robot Coordination
    Mentor: Dr. Kagan Tumer

    Many interesting real world problems require multiple robots to work together. For example, search and rescue missions require coordinating dozens of autonomous robots, as well as ensuring that the robots and humans work together. But providing hard-coded coordination instructions is too limiting. This project explores the science of coordination, and focuses on how to provide incentives to individual robots so that they work collectively.

    Example projects:

    • Programming intelligent decision making for robots
    • Implementing incentives for robots
    • Testing coordination algorithms in hardware (wheeled and flying robots)