Low-cost legged robot, USA

[Airicist2]

Researchers at Carnegie Mellon University's School of Computer Science and the University of California, Berkeley, have designed a robotic system that enables a low-cost and relatively small legged robot to climb and descend stairs nearly its height; traverse rocky, slippery, uneven, steep and varied terrain; walk across gaps; scale rocks and curbs; and even operate in the dark.

[Airicist2]

CMU, Berkeley researchers design system creating robust legged robot

Nov 15, 2022

"A Low-Cost Robot Ready for any Obstacle"
CMU, Berkeley Researchers Design System Creating Robust Legged Robot

by Aaron Aupperlee
November 16, 2022

This little robot can go almost anywhere.

Researchers at Carnegie Mellon University’s School of Computer Science and the University of California, Berkeley, have designed a robotic system that enables a low-cost and relatively small legged robot to climb and descend stairs nearly its height; traverse rocky, slippery, uneven, steep and varied terrain; walk across gaps; scale rocks and curbs, and even operate in the dark.

“Empowering small robots to climb stairs and handle a variety of environments is crucial to developing robots that will be useful in people’s homes as well as search-n-rescue operations,” said Deepak Pathak, an assistant professor at the Robotics Institute. “This system creates a robust and adaptable robot that could perform many everyday tasks.”

Deepak Pathak

[Airicist2]

Legged robot performing extreme parkour [more results]

Oct 9, 2023

Extreme Parkour with Legged Robots
Authors: Xuxin Cheng*, Kexin Shi*, Ananye Agarwal, Deepak Pathak

TLDR: A low-cost robot does extreme parkour including high jumps on obstacles 2x its height, long jumps across gaps 2x its length, handstand on stairs, and running across tilted ramps.

Abstract: Humans can perform parkour by traversing obstacles in a highly dynamic fashion requiring precise eye-muscle coordination and movement. Getting robots to do the same task requires overcoming similar challenges. Classically, this is done by independently engineering perception, actuation, and control systems to very low tolerances. This restricts them to tightly controlled settings such as a predetermined obstacle course in labs. In contrast, humans are able to learn parkour through practice without significantly changing their underlying biology. In this paper, we take a similar approach to developing robot parkour on a small low-cost robot with imprecise actuation and a single front-facing depth camera for perception which is low-frequency, jittery, and prone to artifacts. We show how a single neural net policy operating directly from a camera image, trained in simulation with large-scale RL, can overcome imprecise sensing and actuation to output highly precise control behavior end-to-end. We show our robot can perform a high jump on obstacles 2x its height, long jump across gaps 2x its length, do a handstand and run across tilted ramps, and generalize to novel obstacle courses with different physical properties.