Waymo recently announced two new partnerships for their fleet of robotaxis.

A few weeks ago we had the kick-off meeting, in York, of our new 4 year EPSRC funded project Autonomous Robot Evolution (ARE): cradle to grave. We – Andy Tyrrell and Jon Timmis (York), Emma Hart (Edinburgh Napier), Gusti Eiben (Free University of Amsterdam) and myself – are all super excited. We’ve been trying to win support for this project for five years or so, and only now succeeded. This is a project that we’ve been thinking, and writing about, for a long time – so to have the opportunity to try out our ideas for real is wonderful.

Robots become every day more ‘intelligent’. What if robots were intelligent enough to say NO to war? This would be a happier future.

This short film is a light-hearted comedy that aims to launch an interesting discussion and motivate reflexion on the killer-robots topic. The fictional scenario describes a future where robots contract out and refuse to be employed in human warfare. This optimistic point of view can be inspirational to engineers and roboticists developing a robotic future.

For a lot of people, being a passenger in a car can easily lead to motion sickness, particularly if they try to do something like looking down to read a book or stare at a phone. Not everybody gets this, but it’s enough to be a big issue for the robocar world. Drivers usually don’t feel this much, but in the robocar world, everybody’s a passenger.

Ever since the première of “Steamboat Willie” in 1928, The Walt Disney Company has pushed the envelope of imagination. Mickey Mouse is still more popular worldwide than any single human actor. In fact, from that one cel an entire world of animated characters was born. The entertainment powerhouse demonstrated last week a new generation of theatrics with a flying robot-like stuntman (hero pause and all) that is destined to become a leading player in the age of autonomy.

In discussion of the eventual cost of a robotaxi ride, I and others have forecast costs similar to the all-in cost of car ownership. Today that’s 40 to 60 cents/mile (plus parking) and for a one person electric “city car” it can be under 20 cents.

Readers of this blog will know that I’ve become very excited by the potential of robots with simulation-based internal models in recent years. So far we’ve demonstrated their potential in simple ethical robots and as the basis for rational imitation. Our most recent publication instead examines the potential of robots with simulation-based internal models for safety. Of course it’s not hard to see why the ability to model and predict the consequences of both your own and others’ actions can help you to navigate the world more safely than without that ability.

Our paper Simulation-Based Internal Models for Safer Robots demonstrates the value of anticipation in what we call the corridor experiment. Here a smart robot (equipped with a simulation based internal model which we call a consequence engine) must navigate to the end of a corridor while maintaining a safe space around it at all times despite five other robots moving randomly in the corridor – in much the same way you and I might have to navigate down a busy office corridor while others are coming in the opposite direction.

Here is the abstract from our paper:

In this paper, we explore the potential of mobile robots with simulation-based internal models for safety in highly dynamic environments. We propose a robot with a simulation of itself, other dynamic actors and its environment, inside itself. Operating in real time, this simulation-based internal model is able to look ahead and predict the consequences of both the robot’s own actions and those of the other dynamic actors in its vicinity. Hence, the robot continuously modifies its own actions in order to actively maintain its own safety while also achieving its goal. Inspired by the problem of how mobile robots could move quickly and safely through crowds of moving humans, we present experimental results which compare the performance of our internal simulation-based controller with a purely reactive approach as a proof-of-concept study for the practical use of simulation-based internal models.

So, does it work? Thanks to some brilliant experimental work by Christian Blum the answer is a resounding yes. The best way to understand what’s going on is with this wonderful gif animation of one experimental run below. The smart robot (blue) starts at the left and has the goal of safely reaching the right hand end of the corridor – its actual path is also shown in blue. Meanwhile 5 (red) robots are moving randomly (including bouncing off walls) and their actual paths are also shown in red; these robots are equipped only with simple obstacle avoidance behaviours. The larger blue circle shows blue’s ‘attention radius’ – to reduce computational effort blue will only model red robots within this radius. The yellow paths in front of the red robots in blue’s attention radius show blue’s predictions of how those robots will move (taking into account collisions with the corridor walls and with blue and each other). The light blue projection in front of blue shows which of the 34 next possible actions of blue that is internally modelled is actually chosen as the next action (which, as you will see, sometimes includes standing still).

What do the results show us? Christian ran lots of trials – 88 simulations and 54 real robot experiments – over four experiments: (1) the baseline in simulation – in which the blue robot has only a simple reactive collision avoidance behaviour, (2) the baseline with real robots, (3) using the consequence engine (CE) in the blue robot in simulation, and (4) using the consequence engine in the blue robot with real robots. In the results below (a) shows the time taken for the blue robot to reach the end of the corridor, (b) shows the distance that the blue robot covers while reaching the end of the corridor, (c) shows the “danger ratio” experienced by the blue robot, and (d) shows the number of consequence engine runs per timestep in the blue robot. The danger ratio is the percentage of the run time that anther robot is within the blue robot’s safety radius.

For a relatively small cost in additional run time and distance covered, panels (a) and (b), the danger ratio is very significantly reduced from a mean value of ~20% to a mean value of zero, panel (c). Of course there is a computational cost, and this is reflected in panel (d); the baseline experiment has no consequence engine and hence runs no simulations, whereas the smart robot runs an average of between 8 and 10 simulations per time-step. This is exactly what we would expect: predicting the future clearly incurs a computational overhead.

The Tempe police released a detailed report on their investigation of Uber’s fatality. I am on the road and have not had time to read it, but the big point, reported in many press was that the safety driver was, according to logs from her phone accounts, watching the show “The Voice” via Hulu on her phone just shortly before the incident.

Here are the slides from my York Festival of Ideas keynote yesterday, which introduced the festival focus day Artificial Intelligence: Promises and Perils.

Sitting in New York City, looking up at the clear June skies, I wonder if I am staring at an endangered phenomena. According to many in the Unmanned Aircraft Systems (UAS) industry, skylines across the country soon will be filled with flying cars, quadcopter deliveries, emergency drones, and other robo-flyers. Moving one step closer to this mechanically-induced hazy future, General Electric (GE) announced last week the launch of AiRXOS, a “next generation unmanned traffic” management system.

If the robotics world had a celebrity it would be Spot Mini of Boston Dynamics. Last month at the Robotics Summit in Boston the mechanical dog strutted onto the floor of the Westin Hotel trailed by hundreds of flickering iPhones. Marc Raibert first unveiled his metal menaagerie almost a decade ago with a video of Big Dog. Today, Mini is the fulfillment of his mission in a sleeker, smarter, and environmentally friendlier robo-canine package than its gas-burning ancestor.

Three and half years ago, I stood on the corner of West Street and gasped as two window washers clung to life at the end of a rope a thousand feet above. By the time rescue crews reached the men on the 69th floor of 1 World Trade they were close to passing out from dangling upside down. Everyday risk-taking men and women hook their bodies to metal scaffolds and ascend to deadly heights for $25 an hour. Ramone Castro, a window washer of three decades, said it best, “It is a very dangerous job. It is not easy going up there. You can replace a machine but not a life.” Castro’s statement sounds like an urgent call to action for robots.

Tesla can do better than its current public response to the recent fatal crash involving one of its vehicles. I would like to see more introspection, credibility, and nuance.

The NHTSA/SAE “levels” of robocars are not just incorrect. I now believe they are contributing to an attitude towards their “level 2” autopilots that plays a small, but real role in the recent Tesla fatalities.

The Uber car and Tesla’s autopilot, both in the news for fatalities are really two very different things. This table outlines the difference. Also, see below for some new details on why the Tesla crashed and more.