Contents
28 October 2020
Vol 5, Issue 47
Vol 5, Issue 47
Focus
- Quadrupedal robots trot into the wild
Deep reinforcement learning enables quadruped robots to traverse challenging natural environments using only proprioception.
- Analog-digital computing let robots go through the motions
A hybrid analog-digital computing platform using memristors enables faster, more energy-efficient, and adaptive real-time robot control.
Research Articles
- Learning quadrupedal locomotion over challenging terrain
A learning-based locomotion controller enables a quadrupedal ANYmal robot to traverse challenging natural environments.
- A memristor-based hybrid analog-digital computing platform for mobile robotics
A memristor-based hybrid analog-digital computing platform enables more energy-efficient and responsive mobile robotics.
About The Cover
ONLINE COVER A Trot in the Dark. The ability to traverse deformable surfaces (mud or snow) and dynamic footholds (rubble), as well as handle impediments such as thick vegetation and flowing water, is key to successfully navigating unstructured natural environments. Lee et al. have developed a locomotion controller that uses deep reinforcement learning to teach a quadruped robot how to navigate unseen and unstructured environments without the need for external sensors. The quadruped, ANYmal, was deployed in various outdoor settings to demonstrate that it could robustly traverse a range of challenging terrain relying solely on proprioception. This month's cover is a photograph of ANYmal atop Tenner Chrüz, Tenna, Switzerland (see also the Focus by Ha). [IMAGE CREDIT: SIMON TANNER/NZZ]
