Team member Jeremey Collette says one problem is the switch to a traditional black-and-white ball, as opposed to the previous year’s bright orange ball, which was hard for the visual system programmed by the team to pick out.
The team went to the Pre-robocup Asia Pacific Competition in Beijing in October and took second place to the UT Austin villa team in the orange ball league (there was a separate black-and-white ball league).
One major advantage held by rUNSWift was a balancing algorithm which let the robots run at a breathtaking 30 centimetres a second (1.2 km/h) without falling over (robots fall over a lot in Robosoccer.)
If the ball was in the Australian half, the robots would kick it and rely on their superior speed to run it down. Again, standing upright and running come naturally to humans but has proved immensely difficult fort robots.
That limits the ability of robots to do tasks outside of standardised factory conditions, such as carrying loads up a set of unfamiliar stairs which any human can do.
Collette said rUNSWift’s balancing advantage was fading. One of the rules of Robosoccer is after the competition is over the teams have to release the code they have used to all the other teams, as part of the aim of improving overall skill.
As noted the robots do not have the processing capacity or advanced optical systems of an industrial robots.
They are somewhere above a high-end smart phone with two cameras, and that system has to be programmed to recognise the ball on the field, walk, kick, decide where the robot is on the field, act on the team strategy and decide whether the starting whistle has sounded (another surprisingly hard issue for robots).
Any Robocup game is then a minor miracle and the still clumsy on-field action a vast improvement over games in earlier years.
There are plenty of reports pointing to a wave of automation set to eliminate job categories. A working paper The Future of Employment by Oxford University academics Carl Benedikt Frey and Michael Osborne estimated 47 per cent of jobs in the US are at risk of being automated.
Jobs to be robotised, from the detailed analyses undertaken by the two academics, include truck driving and the bulk of administrative support functions.
Self-driving cars are certainly the topic of the moment and bookkeeping systems now routinely do a lot of the reconciliation work which took hours of office time in past decades.
Automation and artificial intelligence is also continually evolving, with one on the horizon called deep learning. As explained by Toby Walsh, a professor of AI at the University of NSW in an article on The Conversation, “deep learning uses a ‘deep’ neural network, loosely modelled on the human brain. It’s deep because it has half a dozen or so of layers.”
Those permit the neural network to pick out features. For example, in recognising images, the intermediate layers recognise features like edges and corners, Walsh said.
But despite the continual evolution of AI techniques, the near universal use of computers and apps, economists routinely joke they can see the information revolution everywhere but in the productivity figures.
A book The rise and Fall of American Growth by distinguished economics professor Robert J Gordon contends the real change era was between 1870 and 1970 with various phases in that.
He contends nothing really beats the major transformations wrought by, say, the electrification of factories or the mass use of telephones. In offices, do the changes caused by laptops and iPhones really beat the drudge work automated away by the advent of typewriters and later photocopying machines?
Gordon calculates between 1920 and 1970 output per hour in the US increased an average of 2.82 per cent a year. But after 1970 the average increase was 1.62 per cent.
Professor Gordon’s thesis that major changes wrought by IT systems in the past few years are less significant than previous advances remains controversial and productivity comparisons are difficult beasts. But productivity growth tracked by the Australian Bureau of Statistics (see chart) would suggest in Australia the decline began in the mid-1990s.
There are certainly skills which humans learn naturally which computers have a lot of trouble emulating. Scotland Yard, for example, has set up a ‘super recogniser’ task force, a unit staffed by people with an uncanny ability to recognise faces, even in grainy, poor-quality pictures and videos.
This ability is thought to be present in about 1 per cent of the population. Staff scan CTV images and pictures relevant to open cases looking for faces known to the police. The success of the unit has prompted police in other jurisdictions to set up their own.
Such promising advances aside, as technological changes has created and then mostly swept away whole professions, such as switch-board operators and typesetters, only the brave or foolhardy would forecast what will happen next.
But anyone watching a Robocup game, where the players charge around the field looking for a ball any child can see, may think Gordon has a point and technological change that actually affects productivity has slowed down.
Mark Lawson is a retired senior journalist