Skip to content

#ETH Robotics

Today 0 records
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.

2026-09-22Tue
  1. Robotic Systems Lab63

    PragmaBot enables robots to learn from real-world failures through online in-context learning, reflecting on past experiences, storing lessons in memory, and retrieving them for new tasks. This approach avoids the need for retraining, addressing limitations in using VLMs for embodied robotics.

    Editorial context：The post introduces PragmaBot, a system that enables robots to learn from real-world failures through online in-context learning, without requiring retraining. It highlights the challenge of using Vision-Language Models (VLMs) in robotics, where understanding physical embodiment is critical for effective learning.

2026-09-15Tue