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Ensuring that robots can smoothly collaborate with humans in real-world environments is a crucial step towards their large-scale deployment. While some robotic systems are already engaging daily with human agents, for instance at partially automated industrial and manufacturing facilities, human-robot collaboration on everyday tasks remains scarce.

Researchers at University of Padova and Mitsubishi Electric Research Laboratories (MERL) in Cambridge have developed a framework that helps to plan tasks that involve human-robot collaboration. This framework, introduced in a paper pre-published on the arXiv server, specifically focuses on tasks that entail the collaborative assembly of complex systems with various underlying components, such as pieces of furniture.

The researchers called their framework DECAF, which stands for Discrete-Event based Collaborative Human-Robot Assembly Framework for furniture. DECAF has various underlying components, including a discrete-event Markov decision process (DE-MDP) model, a HTM description of the assembly process and a Bayesian interference module.

"The human is characterized as an uncontrollable agent, implying, for example, that the agent is not bound by a pre-established sequence of actions and instead acts according to its own preferences," Giulio Giacomuzzo, Matteo Terreran and their colleagues wrote in their paper. "Meanwhile, the task planner computes reactively the optimal actions for the collaborative robot to efficiently complete the entire assembly task in the least time possible."

With the newly developed framework, the collaborative assembly process spans across various steps. Firstly, the robot observes the actions performed by the human agent, via a camera or other sensors.

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