In robotics, there are two types of control. First there is the "low-level" control needed to co-ordinate the actions of motors. For example, the speed of wheels or the movement of a joint. Then there is the "high-level" control needed to carry out specific goals using the whole system. For example, picking something up, then carrying it to a target.
The ideal outcome of the DARPA challenge would have been a robot that could complete the challenge autonomously, without any human control. In fact, all of the high-level control was performed by human operators (via remote control). Some of the lower-level control was also done in this way, including, in some cases, deciding where the robot should place its feet when walking.
The reason high-level autonomy was not more prominent in the competition was the difficulty of creating and operating the hardware needed to perform the tasks. Most teams chose robots with a human-like body shape - although the winner extended human capabilities with wheeled knees and rotating waist - even though the rules didn't limit them in this way. In order for a humanoid robot to perform an action with one part of its body, the rest of its body must also be co-ordinated to counteract the forces involved.
For example, for a robot to push a power tool through a wall it must generate enough force to push while also altering its balance to prevent itself from falling over due to recoil. This kind of co-ordination happens in a very high-dimensional space, meaning parts have to be moved in many different directions - a complexity that is very hard to model computationally.
This difficulty meant that the majority of effort in the DARPA challenge went towards low-level control algorithms. Although this may be disappointing to those interested in fully autonomous robots, developing low-level control was actually one of the main intentions of the competition. Robust high-level autonomy can only be created once the lower-level systems are robust and reliable.
The difference is striking if you compare DARPA's Robotics Challenge to its Urban Challenge, in which teams competed to deliver self-driving cars. In this competition, the physical engineering tasks were mature and well-understood - we've been building working cars for more than 100 years. The result was a highly impressive display of autonomy as the engineers were able to concentrate on high-level control software.
The Robotics Challenge should be seen as just the beginning. As the physical bodies and low-level control software of humanoid robots improve, scientists can start to create the first complex autonomous behaviours for large-scale humanoids. So, when the next competition happens, we may see these machines thinking for themselves a little more.
Nick Hawes is senior lecturer in intelligent robotics at the University of Birmingham.
