Helping robots handle uncertainty
Decentralized partially observable Markov decision processes are a way to model autonomous robots’ behavior in circumstances where neither their communication with each other nor their judgments about the outside world are perfect. The problem with Dec-POMDPs (as they’re abbreviated) is that they’re as complicated as their name. They provide the most rigorous mathematical models of multiagent systems — not just robots, but any autonomous networked devices —under uncertainty. But for all but the simplest cases, they’ve been prohibitively time-consuming to solve. Last summer, MIT researchers presented a paper that made Dec-POMDPs much more practical for real-world robotic systems. They showed that Dec-POMDPs could determine the optimal way to stitch together existing, lower-level robotic control systems to accomplish collective tasks. By sparing Dec-POMDPs the nitty-gritty details, the approach made them computationally tractable. At this year’s International Conference on Robotics and Automation, another team of MIT researchers takes this approach a step further. Their new...