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#Locomotion

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2026-09-29Tue
  1. Robotic Systems Lab62

    The team at ETH Robotic Systems Lab demonstrated high-speed rough-terrain locomotion on ANYmal using an automatic curriculum learning approach called LP-ACRL. This method dynamically samples terrain types, difficulty levels, and velocity commands based on policy performance, without requiring pre-defined training sequences. The work was presented at RAL 2026.

    Editorial context：The work introduces LP-ACRL, a curriculum learning framework that automatically samples terrain types, difficulty levels, and velocity commands based on policy performance, eliminating the need for pre-defined training sequences.

2026-08-17Mon
2026-06-10Wed
  1. Unitree15

    We’re excited to partner with BitRobot Network, Lightwheel AI, Singapore Institute of Technology and contributors like Jie Tan (Deepmind), Steve Xie (Lightwheel), Michael Cho (FrodoBots), etc. Looking forward to push the boundary of humanoid loco-manipulation in this Humanoid IKEA Assembly Challenge!

    Quoted postBitRobot 🦾@BitRobotNetwork

    Can humanoids assemble IKEA furniture? We’re inviting researchers to test their policies at the Humanoid IKEA Assembly Challenge at @ieeeras IROS 2026. Co-organized with @UnitreeRobotics, @LightwheelAI, @singaporetech, and more. We’re providing all the resources, more info ↓