ETH engineers modified ANYMal with hook-like paws and reinforcement learning, achieving 90% ladder-climbing success in real-world tests.
The upgrade enhances its mobility in complex environments, making it more versatile for tasks like inspection and rescue operations.
Even while legged robots are superior to wheeled counterparts in difficult and uneven environments, they are still unable to consistently navigate one of the most common elements of industrial infrastructure: ladders. Studies on quadrupedal robots also faced similar constraints, demonstrating slow ascent on only vertical ladders. Beyond ladder climbing, robust locomotion in quadrupeds has been achieved in challenging terrains using model-based methods, but these are sensitive to disturbances.
They created a special paw with a hook-like mechanism that could tightly grasp and clasp onto ladder rungs in order to address this. The robot was then trained to use these hooks for climbing via
Anymal Climbing Robots ETH Reinforcement Learning Robot
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