All posts by Oregon State University

Scientists and robot at White Sands National Park.

By Sean Nealon

Researchers are closer to equipping a dog-like robot to conduct science on the surface of Mars after five days of experiments this month at White Sands National Park in New Mexico.

The national park is serving as a Mars analog environment and the scientists are conducting field test scenarios to inform future Mars operations with astronauts, dog-like robots known as quadruped robots, rovers and scientists at Mission Control on Earth. The work builds on similar experiments by the team with the same robot on the slopes of Mount Hood in Oregon, which simulated the landscape on the Moon.

“Our group is very committed to putting quadrupeds on the Moon and on Mars,” said Cristina Wilson, a robotics researcher in the College of Engineering at Oregon State University. “It’s the next frontier and takes advantage of the unique capabilities of legged robots.”

The NASA-funded project supports the agency’s Moon to Mars program, which is developing the tools for long-term lunar exploration and future crewed missions to Mars. It builds on research that has enabled NASA to send rovers and a helicopter to Mars.

The LASSIE Project: Legged Autonomous Surface Science in Analog Environments includes engineers, cognitive scientists, geoscientists and planetary scientists from Oregon State, the University of Southern California, Texas A&M University, the Georgia Institute of Technology, the University of Pennsylvania, Temple University and NASA Johnson Space Center.

The field work this month at White Sands was the second time the research team visited the national park. They made the initial trip in 2023 and also made trips in 2023 and 2024 to Mount Hood. During these field sessions, the scientists gather data from the feet of the quadruped robots, which can measure mechanical responses to foot-surface interactions.

“In the same way that the human foot standing on ground can sense the stability of the surface as things shift, legged robots are capable of potentially feeling the exact same thing,” Wilson said. “So each step the robot takes provides us information that will help its future performance in places like the Moon or Mars.”

Quadruped robot.

The conditions at White Sands this month were challenging. Triple-digit high temperatures meant the team started field work at sunrise and wrapped by late morning because of the rising heat index and its impact on the researchers and the power supply to the robots.

But the team made important progress. Improvements to the algorithms they have refined in recent years led for the first time to the robot acting autonomously and making its own decisions.

This is important, Wilson noted, because in a scenario where the quadruped would be on the surface of Mars with an astronaut, it would allow both the robot and the astronaut to act independently, increasing the amount of scientific work that could be accomplished.

They also tested advances they have made in developing different ways for the robot to move depending on surface conditions, which could lead to increased energy efficiency, Wilson said.

“There is certainly a lot more research to do, but these are important steps in realizing the goal of sending quadrupeds to the Moon and Mars,” Wilson said.

Other leaders of the project include Feifei Qian, USC; Ryan Ewing and Kenton Fisher, NASA Johnson Space Center; Marion Nachon, Texas A&M; Frances Rivera-Hernández, Georgia Tech; Douglas Jerolmack and Daniel Koditschek, University of Pennsylvania; and Thomas Shipley, Temple University.

The research is funded by the NASA Planetary Science and Technology through Analog Research (PSTAR) program, and Mars Exploration Program.

Cassie the robot sets 100-metre record, photo by Kegan Sims.

By Steve Lundeberg

Cassie the robot, invented at the Oregon State University College of Engineering and produced by OSU spinout company Agility Robotics, has established a Guinness World Record for the fastest 100 metres by a bipedal robot.

Cassie clocked the historic time of 24.73 seconds at OSU’s Whyte Track and Field Center, starting from a standing position and returning to that position after the sprint, with no falls.

The 100-metre record builds on earlier achievements by the robot, including traversing five kilometres in 2021 in just over 53 minutes. Cassie, the first bipedal robot to use machine learning to control a running gait on outdoor terrain, completed the 5K on Oregon State’s campus untethered and on a single battery charge.

Cassie was developed under the direction of Oregon State robotics professor Jonathan Hurst. The robot has knees that bend like an ostrich’s and operates with no cameras or external sensors, essentially as if blind.

Since Cassie’s introduction in 2017, in collaboration with artificial intelligence professor Alan Fern, OSU students have been exploring machine learning options in Oregon State’s Dynamic Robotics and AI Lab.

“We have been building the understanding to achieve this world record over the past several years, running a 5K and also going up and down stairs,” said graduate student Devin Crowley, who led the Guinness effort. “Machine learning approaches have long been used for pattern recognition, such as image recognition, but generating control behaviors for robots is new and different.”

The Dynamic Robotics and AI Lab melds physics with AI approaches more commonly used with data and simulation to generate novel results in robot control, Fern said. Students and researchers come from a range of backgrounds including mechanical engineering, robotics and computer science.

“Cassie has been a platform for pioneering research in robot learning for locomotion,” Crowley said. “Completing a 5K was about reliability and endurance, which left open the question of, how fast can Cassie run? That led the research team to shift its focus to speed.”

Cassie was trained for the equivalent of a full year in a simulation environment, compressed to a week through a computing technique known as parallelization – multiple processes and calculations happening at the same time, allowing Cassie to go through a range of training experiences simultaneously.

“Cassie can perform a spectrum of different gaits but as we specialized it for speed we began to wonder, which gaits are most efficient at each speed?” Crowley said. “This led to Cassie’s first optimized running gait and resulted in behavior that was strikingly similar to human biomechanics.”

The remaining challenge, a “deceptively difficult” one, was to get Cassie to reliably start from a free-standing position, run, and then return to the free-standing position without falling.

“Starting and stopping in a standing position are more difficult than the running part, similar to how taking off and landing are harder than actually flying a plane,” Fern said. “This 100-metre result was achieved by a deep collaboration between mechanical hardware design and advanced artificial intelligence for the control of that hardware.”

Hurst, chief technology officer at Agility Robotics and a robotics professor at Oregon State, said: “This may be the first bipedal robot to learn to run, but it won’t be the last. I believe control approaches like this are going to be a huge part of the future of robotics. The exciting part of this race is the potential. Using learned policies for robot control is a very new field, and this 100-metre dash is showing better performance than other control methods. I think progress is going to accelerate from here.”