Humanoids, robot dogs master unseen terrains with attention mapping

Researchers at ETH Zurich have developed an advanced control system for legged robots, including the quadrupedal ANYmal-D and humanoid Fourier GR-1, enabling them to navigate complex and previously unseen terrains. This system employs a machine learning technique called attention-based map encoding, trained via reinforcement learning, which allows the robot to focus selectively on the most critical areas of a terrain map rather than processing the entire map uniformly. This focused attention helps the robots identify safe footholds even in challenging environments, improving robustness and generalization across varied terrains. The system demonstrated successful real-time locomotion at speeds up to 2 meters per second, with notably low power consumption relative to the robot’s motors. While the current approach is limited to 2.5D height-map locomotion and cannot yet handle overhanging 3D obstacles such as tree branches, the researchers anticipate extending the method to full 3D environments and more complex loco-manipulation tasks like opening doors or climbing. The attention mechanism also provides