Carnegie Mellon researchers have developed an open-source software that enables more agile movement in legged robots.

Robots can help humans with tasks like aiding disaster recovery efforts or monitoring the environment. In the case of quadrupeds, robots that walk on four legs, their mobility requires many software components to work together seamlessly. Most researchers must spend much of their time developing lower-level infrastructure instead of focusing on high-level behaviors.

Aaron Johnson’s team in the Robomechanics Lab at Carnegie Mellon University’s College of Engineering has experienced these frustrations firsthand. The researchers have often had to rely on simple models for their work because existing software solutions were not open-sourced, did not provide a modular framework, and lacked end-to-end functionality.

This same feature could also play a role in our ability to tell what is human, and what is not. Several experiments have shown that when humanoid robots exhibit human-like variability in response times or motion patterns, we perceive them as more human-like.

In a study published in Science Robotics¹, researchers have observed this same effect when the human and the robot are performing a shared activity. “To evaluate the impact of behavioural variability in the attribution of humanness to a robot, in our experiment the robot was either teleoperated by another human or controlled by a computer”, says Agnieszka Wykowska, senior researcher at the Italian Institute of Technology (IIT) in Genoa, and the coordinator of the study.

The research has also shown that the effect applies even when the variability of the robot’s behaviour does not closely resemble the human one, if it falls in the same range. “Depending on the context and on the function that the robot needs to perform, roboticists can endow their machines with a different degree of humanness by modulating the variability of their behaviors,” Wykowska adds.

Even the simplest human tasks are unbelievably complex. The way we perceive and interact with the world requires a lifetime of accumulated experience and context. For example, if a person tells you, “I am running out of time,” you don’t immediately worry they are jogging on a street where the space-time continuum ceases to exist. You understand that they’re probably coming up against a deadline. And if they hurriedly walk toward a closed door, you don’t brace for a collision, because you trust this person can open the door, whether by turning a knob or pulling a handle.

The latest language models are amazing. But for general artificial intelligence, I suspect we’re barking up the wrong tree.

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It’s odd to try to create artificial intelligence without taking inspiration from the only existing natural intelligences we have: humans and animals. Yet that is the current direction of AI research.