Collaborating Authors

Robot 'Natural Selection' Recombines Into Something Totally New


Worms, mammals, even bees do it. Every living thing on Earth replicates, whether that be asexually (boring) or sexually (fun). Robots do not do it: The machines are steely and very uninterested in reproduction. But perhaps they can learn. Scientists in a fascinating field known as evolutionary robotics are trying to get machines to adapt to the world, and eventually to reproduce on their own, just like biological organisms.

Countering the Frankenstein Complex

AAAI Conferences

Isaac Asimov coined the term "Frankenstein Complex" to describe the fear that the general public has towards humanmade technologies when they invade the realm commonly considered to be God's domain. Recently, several people have made a name for themselves partially by fanning this flame. This poster demonstrates some of the historical evidence of this fear and provides reasons why it is unfounded. Finally, it suggests a way for the AI community to help ameliorate the public's fear of a marauding species of homicidal robots.

This is how the robot uprising finally begins

MIT Technology Review

The robot arm is performing a peculiar kind of Sisyphean task. It hovers over a glistening pile of cooked chicken parts, dips down, and retrieves a single piece. A moment later, it swings around and places the chunk of chicken, ever so gently, into a bento box moving along a conveyor belt. This robot, created by a San Francisco–based company called Osaro, is smarter than any you've seen before. The software that controls it has taught it to pick and place chicken in about five seconds--faster than your average food-processing worker.

What is artificial intelligence? (Or, can machines think?)


Here are the slides from my York Festival of Ideas keynote yesterday, which introduced the festival focus day Artificial Intelligence: Promises and Perils. I start the keynote with Alan Turing's famous question: Can a Machine Think? and explain that thinking is not just the conscious reflection of Rodin's Thinker but also the largely unconscious thinking required to make a pot of tea. I note that at the dawn of AI 60 years ago we believed the former kind of thinking would be really difficult to emulate artificially and the latter easy. In fact it has turned out to be the other way round: we've had computers that can expertly play chess for 20 years, but we can't yet build a robot that could go into your kitchen and make you a cup of tea. In slides 5 and 6 I suggest that we all assume a cat is smarter than a crocodile, which is smarter than a cockroach, on a linear scale of intelligence from not very intelligent to human intelligence.

Data-efficient Learning of Morphology and Controller for a Microrobot Artificial Intelligence

Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design. For most robots, this process is further complicated by the need, when validating the capabilities of the hardware to solve the desired task, to already have an appropriate controller, which is in turn designed and tuned for the specific hardware. In this paper, we propose a novel approach, HPC-BBO, to efficiently and automatically design hardware configurations, and evaluate them by also automatically tuning the corresponding controller. HPC-BBO is based on a hierarchical Bayesian optimization process which iteratively optimizes morphology configurations (based on the performance of the previous designs during the controller learning process) and subsequently learns the corresponding controllers (exploiting the knowledge collected from optimizing for previous morphologies). Moreover, HPC-BBO can select a "batch" of multiple morphology designs at once, thus parallelizing hardware validation and reducing the number of time-consuming production cycles. We validate HPC-BBO on the design of the morphology and controller for a simulated 6-legged microrobot. Experimental results show that HPC-BBO outperforms multiple competitive baselines, and yields a $360\%$ reduction in production cycles over standard Bayesian optimization, thus reducing the hypothetical manufacturing time of our microrobot from 21 to 4 months.