Mehdi Dadvar - Deliberative Communication Planning

Deliberative Communication Planning

The long-lasting idea of human-robot collaboration has thrived remarkably in the last decade and cobots are being employed increasingly to provide diversified services which has led to more and more human-robot co-working environments. In practice, however, there are pervasive situations in which inherent constraints of the co-working environment jeopardize the safety and task achievability of the scenario. In this project, we aim to address this problem in various settings by exploring deeper modalities of the human-robot interactions, such as implicit and explicit communication.

As the first step, we explored the explained problem in social navigation settings:

Joint Communication and Motion Planning for Cobots

M. Dadvar, K. Majd, E. Oikonomou, G. Fainekos, S. Srivastava

[BibTex] [PDF] [Extended Version] [Poster] [Project Webpage] IEEE Int'l Conf. on Robotics and Automation (ICRA) 2022

abstract: The increasing deployment of robots in co-working scenarios with humans has revealed complex safety and efficiency challenges in the computation robot behavior. Movement among humans is one of the most fundamental —and yet critical— problems in this frontier. While several approaches have addressed this problem from a purely navigational point of view, the absence of a unified paradigm for communicating with humans limits their ability to prevent deadlocks and compute feasible solutions. This paper presents a joint communication and motion planning framework that selects from an arbitrary input set of robot's communication signals while computing robot motion plans. It models a human co-worker's imperfect perception of these communications using a noisy sensor model and facilitates the specification of a variety of social/workplace compliance priorities with a flexible cost function. Theoretical results and simulator-based empirical evaluations show that our approach efficiently computes motion plans and communication strategies that reduce conflicts between agents and resolve potential deadlocks.