In 2016, I wrote the first installment on this futuristic concern (click here) but now it's getting even more intense.
Quadruped robots are proliferating in industrial environments where they carry sensor suites and serve as autonomous inspection platforms. Despite the advantages of legged robots over their wheeled counterparts on rough and uneven terrain, they are still yet to be able to reliably negotiate ubiquitous features of industrial infrastructure: ladders. Inability to traverse ladders prevents quadrupeds from inspecting dangerous locations, puts humans in harm's way, and reduces industrial site productivity.
In this paper, we learn quadrupedal ladder climbing via a reinforcement learning-based control policy and a complementary hooked end-effector. We evaluate the robustness in simulation across different ladder inclinations, rung geometries, and inter-rung spacings. On hardware, we demonstrate zero-shot transfer with an overall 90% success rate at ladder angles ranging from 70° to 90°, consistent climbing performance during unmodeled perturbations, and climbing speeds 232x faster than the state of the art.
This work expands the scope of industrial quadruped robot applications beyond inspection on nominal terrains to challenging infrastructural features in the environment, highlighting synergies between robot morphology and control policy when performing complex skills. More information can be found at the project website.
My continuing lack of enthusiasm comes from 1984 film Runaway, starring Tom Selleck and Gene Simmons from the rock group KISS. Michael Crichton wrote and directed this movie, which featured fence-climbing hexapod insect robots with acid-tipped stingers, which I continue to hope roboticists are not working on.
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A System To Defeat AI Face Recognition
'...points and patches of light... sliding all over their faces in a programmed manner that had been designed to foil facial recognition systems.'