Where could machines replace humans?

This is the primary question shaping much of the public debate and discourse around the development of autonomous systems technologies, robots being the most visible and eye catching example – occasionally quite scary when you see them being used, e.g., as carriers of weapons and bombs. As a roboticist who is often deeply ensconced in the technical side of developing capabilities, I find most public articles in the popular media to be ill-informed and hyperbolic. So, I was pleasantly surprised to read this McKinsey report. This is not quite popular media, but in the past I have found even some of these consulting company reports to be formulaic. The key point being made by the authors is that just because something can be automated does not mean it should, or that it will. In reality, a variety of different factors, including economic and social will shape the path of these technologies – something all of us should pragmatically consider.

This key point is summarised in the following excerpt:

Technical feasibility is a necessary precondition for automation, but not a complete predictor that an activity will be automated. A second factor to consider is the cost of developing and deploying both the hardware and the software for automation. The cost of labor and related supply-and-demand dynamics represent a third factor: if workers are in abundant supply and significantly less expensive than automation, this could be a decisive argument against it. A fourth factor to consider is the benefits beyond labor substitution, including higher levels of output, better quality, and fewer errors. These are often larger than those of reducing labor costs. Regulatory and social-acceptance issues, such as the degree to which machines are acceptable in any particular setting, must also be weighed. A robot may, in theory, be able to replace some of the functions of a nurse, for example. But for now, the prospect that this might actually happen in a highly visible way could prove unpalatable for many patients, who expect human contact. The potential for automation to take hold in a sector or occupation reflects a subtle interplay between these factors and the trade-offs among them.


Yet Another Autonomous Fender Bender

So, the Google car has now been in an accident that was clearly its fault. For those who have not yet heard about this, see, e.g., http://spectrum.ieee.org/cars-that-think/transportation/self-driving/google-car-may-actually-have-helped-cause-an-accident. Now, it is unfair to criticise the car on its numerical record – it has clearly covered enormous ground with no serious faults so far. Kudos!!

However, the record has been achieved by being careful and conservative. The real issue arises in a scenario which, in the words of the manager of the project, “is a classic example of the negotiation that’s a normal part of driving — we’re all trying to predict each other’s movements. In this case, we clearly bear some responsibility, because if our car hadn’t moved there wouldn’t have been a collision.” So, the real question is when and how exactly these kinds of predictions will get integrated.

By the time the Google car really does leave the sunny confines of California and go to places where the driving is much more adventurous and the very rules of the road are much of a negotiated truce, the numbers will rise from a single digit number of near-misses to a much more routine occurrence without the capacity for this car to reason explicitly about other drivers and their strategies.

This is a question we worry about quite a bit in my group, ranging from lab-scale human subject experiments, e.g.,


to theoretical ideas for how best to achieve such interaction, e.g., http://arxiv.org/abs/1507.07688 (official version: doi:10.1016/j.artint.2016.02.004). We do not directly have the opportunity to contribute to what is running in the car, but I do hope such ideas make their way into it, to fully realise the potential of this fascinating new technology!

Lessons from a robotics entrepreneur

Due to a variety of activities at work, I have been following the activities of some of the alumni of Willow Garage. In part, I am curious about what they do in terms of products and technologies, in light of what they learnt from building and popularising robots like the PR2. 

Recently, I came across this post written by Steve Cousins, who is now the head of Savioke. In this post, he takes on the question about lessons learnt:


It is clearly written from the industry perspective, but I think it is highly relevant to academics too. After all, a lot of the recent attention we are getting as a field is not so much because we have created something new that simply did not exist 20 years back. Instead, it is because even some of these new technologies in our field are finally looking close to being commercially relevant.

The Longitude Prize

The Longitude Prize is an interesting concept. Way back in 1714, it was an award for the person(s) who best solved the difficult problem of determining the ship’s longitude – in that day and age, a real grand challenge, one that literally determined the safety and lives of plenty of people!

Today, the problems of clocks and chronometers may seem quaint, but the notion of defining some big problems that need solving remains interesting. All the more so, in my local context, given the short-termism of so many sources of funding that are realistically available to researchers.

So, the announcement today that there is a new version of this prize is interesting indeed. They are all big societal issues, but one of them stands out in particular as having something to do with problems I am professionally interested in. There is a challenge associated with Dementia – How can we help people with dementia live independently for longer?

Assisted living advocates, including within the robotics and AI communities have built systems associated with this problem before, but can all that be lifted up to the standards of the longitude prize? What are the substantial questions that still remain un answered in this area? Useful things to ponder…

Driverless cars – how hard is it?

Some would claim, not all that hard, depending on what you mean by ‘driver-less’.

I am visiting Bangalore right now and people here are quite amused by this: http://www.team-bhp.com/forum/street-experiences/137424-driverless-esteem-spotted-bangalore-edit-hoax-see-video-page-4-a.html.

Even if it really is ‘drive by wire’ rather than fully autonomous, depending on how much the ‘minor’ joystick control amounts to, it is quite impressive that they can perform a semi-supervised execution of an off-line-map-informed and GPS-enabled plan on the crowded Indian street!

RoboCup TV

Our team, Edinferno, is getting ready for the first match of this robot football world cup season, in Eindhoven.

Some of the games are being broadcast on streaming video:

As is typical, a lot of elbow grease has gone into the creation of our robot team. The human team behind the curtain mainly consists of Aris Valtazanos, Efstathios Vafeias, Alejandro Bordallo Mico and Nantas Nardelli. I am proud of their work!