Enlarge / A kitty named Clover prepares to play with a robot arm in the Cat Royale "multi-species" science/art installation . (credit: Blast Theory - Stephen Daly)

Cats and robots are a winning combination, as evidenced by all those videos of kitties riding on Roombas. And now we have Cat Royale, a "multispecies" live installation in which three cats regularly "played" with a robot over 12 days, carefully monitored by human operators. Created by computer scientists from the University of Nottingham in collaboration with artists from a group called Blast Theory, the installation debuted at the World Science Festival in Brisbane, Australia, last year and is now a touring exhibit. The accompanying YouTube video series recently won a Webby Award, and a paper outlining the insights gleaned from the experience was similarly voted best paper at the recent Computer-Human Conference (CHI’24).

"At first glance, the project is about designing a robot to enrich the lives of a family of cats by playing with them," said co-author Steve Benford of the University of Nottingham, who led the research, "Under the surface, however, it explores the question of what it takes to trust a robot to look after our loved ones and potentially ourselves." While cats might love Roombas, not all animal encounters with robots are positive: Guide dogs for the visually impaired can get confused by delivery robots, for example, while the rise of lawn mowing robots can have a negative impact on hedgehogs, per Benford et al.

Blast Theory and the scientists first held a series of exploratory workshops to ensure the installation and robotic design would take into account the welfare of the cats. "Creating a multispecies system—where cats, robots, and humans are all accounted for—takes more than just designing the robot," said co-author Eike Schneiders of Nottingham's Mixed Reality Lab about the primary takeaway from the project. "We had to ensure animal well-being at all times, while simultaneously ensuring that the interactive installation engaged the (human) audiences around the world. This involved consideration of many elements, including the design of the enclosure, the robot, and its underlying systems, the various roles of the humans-in-the-loop, and, of course, the selection of the cats.”

Enlarge (credit: boonchai wedmakawand)

Reinforcement-learning algorithms in systems like ChatGPT or Google’s Gemini can work wonders, but they usually need hundreds of thousands of shots at a task before they get good at it. That’s why it’s always been hard to transfer this performance to robots. You can’t let a self-driving car crash 3,000 times just so it can learn crashing is bad.

But now a team of researchers at Northwestern University may have found a way around it. “That is what we think is going to be transformative in the development of the embodied AI in the real world,” says Thomas Berrueta who led the development of the Maximum Diffusion Reinforcement Learning (MaxDiff RL), an algorithm tailored specifically for robots.

Introducing chaos

The problem with deploying most reinforcement-learning algorithms in robots starts with the built-in assumption that the data they learn from is independent and identically distributed. The independence, in this context, means the value of one variable does not depend on the value of another variable in the dataset—when you flip a coin two times, getting tails on the second attempt does not depend on the result of your first flip. Identical distribution means that the probability of seeing any specific outcome is the same. In the coin-flipping example, the probability of getting heads is the same as getting tails: 50 percent for each.

Enlarge / A still image of a robotic quadruped armed with a remote weapons system, captured from a video provided by Onyx Industries. (credit: Onyx Industries)

The United States Marine Forces Special Operations Command (MARSOC) is currently evaluating a new generation of robotic "dogs" developed by Ghost Robotics, with the potential to be equipped with gun systems from defense tech company Onyx Industries, reports The War Zone.

While MARSOC is testing Ghost Robotics' quadrupedal unmanned ground vehicles (called "Q-UGVs" for short) for various applications, including reconnaissance and surveillance, it's the possibility of arming them with weapons for remote engagement that may draw the most attention. But it's not unprecedented: The US Marine Corps has also tested robotic dogs armed with rocket launchers in the past.

MARSOC is currently in possession of two armed Q-UGVs undergoing testing, as confirmed by Onyx Industries staff, and their gun systems are based on Onyx's SENTRY remote weapon system (RWS), which features an AI-enabled digital imaging system and can automatically detect and track people, drones, or vehicles, reporting potential targets to a remote human operator that could be located anywhere in the world. The system maintains a human-in-the-loop control for fire decisions, and it cannot decide to fire autonomously.