Socially Assistive Robots for Elder Care
Mentor: Dr. Naomi Fitter

This project aims to assist older adults in living independently in their homes through the use of socially assistive robotics, a field that aims to use robots to improve human health, wellness, communication, learning, and autonomy. By combining sensors, displays, and social robots, this project will blend machine learning, signal processing, and other fields to detect, encourage, and reward healthy human behaviors. REU students working on this project will gain experience in machine learning, computer vision, design, and robotics while working to develop hardware for social interaction and algorithms that can detect and respond to human behaviors.

Example projects:

  • Designing a robotic system that rewards human physical activity
  • Developing a safe robot arm for social touch 
  • Testing and evaluating a social robotic system in an everyday environment like the home or lab

Social and Entertainment robots
Mentor: Dr. Heather Knight

Full description soon: Robots for social and entertainment applications. 

Example projects:

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.


Mentors: Dr. Cindy Grimm and Dr. Ravi Balasubramanian

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.

Example projects:

  • Analyze human data to develop a robotic controller
  • Machine learning and vision techniques to track what happens during grasping
  • Design a robot gripper


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 student projects:


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


Learning from Humans for Robotic Deburring

MentorsDr. Ravi Balasubramanian and Dr. Burak Sencer

Burrs are undesirable projections of material at the edges of a finished part’s surface. They pose a fundamental problem for manufacturing operations since they affect part handling and assembly operations and lead to part failure. Deburring, the process of removing burrs, is currently performed by human operators. But deburring is labor-intensive and causes injury to operators due to the forces and movements involved. This project seeks to learn the deburring process from a human (including forces and movements involved) and develop a robot to automate the deburring process.
Example student projects:

  • Collect and analyze data (motion-capture kinematic data, force data) of a human operator performing the deburring task
  • Develop algorithms for a robotic manipulator that produces similar movements and forces for deburring


Autonomy for Underwater and Surface Vehicles Exploring Ocean Environments

Mentor: Dr. Geoff Hollinger and Dr. Julie A. Adams

There are many ocean environments that are unsuitable for manned research vessels, either because they are too dangerous (e.g., near a calving glacier or in the deep ocean), or require too many resources to be effective. We seek to design a new generation of autonomous underwater and surface vehicles that can be programmed to autonomously measure ocean dynamics in a wide range of environments. This research project involves the design and programing of robust surface and underwater systems that will be used to explore ocean dynamics along the pacific coast, Greenland, Alaska, and in remote ocean basins. The REU student will join an interdisciplinary team of researchers from mechanical engineering, computer science, and oceanography to assist in designing autonomy algorithms and programming marine vehicles at Oregon State University.

Example projects

  • Programming autonomous surface and underwater craft to operate without operator control
  • Simulator design for underwater and surface vehicles
  • Interface design for human operators to coordinate autonomous marine vehicles


Biologically-inspired Soft Robots
Mentor: Dr. Yigit Menguc (external) and Dr. Cindy Grimm (internal)

Biological tissues have rich structural and material compositions that give them elegant mechanical properties, such as the smooth variation in elasticity of a tendon that allows it to connect muscle to bone. We will explore patterning hard and soft materials through additive and traditional manufacturing methods to create structures that can bring biologically-inspired mechanical improvements to soft robots. For instance, one of the challenges in building a soft, squishy robot is constructing durable interfaces between extremely compliant materials such as silicone and rigid parts like batteries.
Example Projects:
  • Designing, fabricating, and testing a bioinspired multimaterial soft robot
  • Developing new methods of fabrication for soft robots with complex geometries or small features
  • 3D printing and testing soft actuators with embedded liquid metal 4) Developing control methodologies and hardware for snake and cephalopod -inspired soft robotsDesigning, fabricating, and testing a bioinspired multimaterial soft robot


Long-term Autonomy for Mobile Robots
Mentor: Dr. Bill Smart

How should robotics algorithms change if we assume that our robots will be operating in the same environment for weeks or months?  Many robot algorithms use a snapshot of sensor data to build a model of the world (like a map) and then use this model for planning.  However, the world is a dynamic place, and the model we build a 10am might look very different to the one we build at 10pm.  The foal of this project is to investigate how to improve the performance of mobile robot systems, under the assumption that they will be used for extended periods of time in the same setting.  Can we incorporate time into our models, and does this help?  Do we have to plan new navigation paths every time we move, or can we rely in previously-calculated information?  How can we get humans to give the robots assistance early on, and then learn from this help to make the robots more autonomous?

Example Projects:

  • Navigation routines tuned for specific tasks, such as passing through doorways, or navigating tight spaces.
  • Long-term autonomy issues: map building and maintenance, data logging and management, and learning from experience.
  • User interface design, implementation, and testing for long-term autonomy.
  • Making existing robotics algorithms temporally aware.


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)


Bat Sonar for Robots

Mentors: Dr. Cindy Grimm and Dr. Kagan Tumer

Bat sonar is an incredibly rich sense - bats can spot and catch insects in the air, fly through dense forest canopy - all by emitting chirps and listening for echos. They accomplish this feat through deformable ear and nose geometries that let them "shape" the sound scape. In this project we aim to build a soft, deformable bat ear and nose in order to mount such a sensor on the robot.

Example projects:

  • Design a soft actuator to bend the ear and nose
  • Experiment with different geometries to see how they shape sound
  • Apply machine learning to map the returned sonar signal to "object detected" in order to let a robot navigate through a space


Robotic Pruning of Fruit Trees

Mentor: Dr. Joe Davidson

The fresh market tree fruit industry is highly labor intensive and is becoming less sustainable with rising costs and shortages of farm labor. Canopy and crop-load management operations, which are essential to improve plant health, control plant size, and increase fruit quality and yield, are highly labor intensive. For example, Washington apple and cherry growers estimate that pruning alone costs about $1,200 - $1,500 per acre per year. Mechanization and automation have the potential to help address these challenges. In this project, we are developing a robotic pruning machine for modern apple orchards.

Example student projects:

  • Help design and fabricate end-effector tooling for branch cutting
  • Study and simulate different robotic pruning trajectories