Ep#3 Sim-to-Real RL for Vision-Based Dexterous Manipulation on Humanoids
Toru, a UC Berkeley PhD student working with Jitendra Malik, discusses vision-based sim-to-real reinforcement learning for humanoid manipulation with RoboPapers hosts.
Victor Suen of Fourier Intelligence introduces the company’s GR-1 humanoid robot and discusses its origins, intended uses, technical focus, and open platform.
Suen presents GR-1 as Fourier Intelligence’s first-generation humanoid robot product. He says the name stands for General Robotics.
Founded in 2015, Fourier began with rehabilitation robotics, including limb-recovery devices, and later developed exoskeletons. Suen says the team found that key robot components were unavailable, so it built many parts from the ground up. After five years of work, GR-1 is available for sale, and the company is continuing to improve it.
Suen sees humanoid robots as a way to improve productivity and take on work that could be dangerous for people. He expects them to support people in a range of activities.
Suen describes a future robot as combining hardware and locomotion with a “brain” for perception, decisions, and human interaction. Fourier focuses mainly on hardware and locomotion, and aims to offer an open platform so other experts can build applications.
Fourier has released its first generation and is developing the next. Suen welcomes companies, researchers, and institutions working on humanoids, describing the field as both competitive and collaborative.
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Toru, a UC Berkeley PhD student working with Jitendra Malik, discusses vision-based sim-to-real reinforcement learning for humanoid manipulation with RoboPapers hosts.
PRO ROBOTS’ presenter reviews humanoid robots and AI systems showcased at WAIC 2026 in China, including demonstrations from AGIBOT, Fourier Intelligence, No Matrix, and others.