Helping robots handle uncertainty

Helping robots handle uncertainty
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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, CSAIL 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 ICRA, another team takes this approach a step further. Their new system can actually generate the lower-level control systems from scratch, while still solving Dec-POMDP models in a reasonable amount of time.

The researchers have also tested their system on a small group of robotic helicopters, in a scenario mimicking the type of drone package delivery envisioned by Amazon and Google, but with the added constraint that the robots can’t communicate with each other.