The image that most of society has of robots is that of slaves -- creations that can be forced do what humans want. Researchers at the Massachusetts Institute of Technology have formed an interesting take on the robot question that is less about slavery, more about cooperation. They observed that language is a function of humans cooperating on tasks, and imagined how robots might use language when working with humans to achieve some result. The word "team" is a word used prominently way up top in the paper, "Decision-Making for Bidirectional Communication in Sequential Human-Robot Collaborative Tasks," written by scientists Vaibhav V. Unhelkar, Shen Li, and Julie A. Shah of the Computer Science and AI Laboratories at MIT and posted on the MIT Web site on March 31st. The use of the word "team" is significant given the structure of the experiment the scientists designed.

In a new study, researchers at MIT's Computer Science and Artificial Intelligence Lab propose a framework called CommPlan, which gives robots that work alongside humans principles for "good etiquette" and leave it to the robots to make decisions that let them finish tasks efficiently. They claim it's a superior approach to handcrafted rules, because it enables the robots to perform cost-benefit analyses on their decisions rather than follow task- and context-specific policies. CommPlan weighs a combination of factors, including whether a person is busy or likely to respond given past behavior, leveraging a dedicated module -- the Agent Markov Model -- to represent that person's sequential decision-making behaviors. It consists of a model specification process and an execution-time partially observable Markov decision process (POMDP) planner, derived as the robot's decision-making model, which CommPlan uses in tandem to arrive at the robot's actions and communications policies. Using CommPlan, developers first specify five modules -- a task model, communication capability, a communication cost model, a human response model, and a human action-selectable model -- with data, domain expertise, and learning algorithms.