We study proactive communicative behavior, where robots provide information to humans which may help them to achieve desired outcomes, or to prevent possible undesired ones. Proactive behavior in an under-addressed area in AI and robotics, and proactive human-robot communication is even more so. We will combine the past expertise of Sorbonne Univ. (intention recognition) and Orebro Univ. (proactive behavior) to define proactive behavior based on the understanding of user’s intentions, and then extend it to consider communicative actions based on second-order perspective awareness.

We propose an architecture able to (1) estimate the human’s intention of goal, (2) infer robot’s and human’s knowledge about foreseen possible upcoming outcomes of intended goal, (3) detect opportunities for desirability of intended goal to robot be proactive, (4) select action from the listed opportunities. The theoretical underpinning of this work will contribute to the study of theory of mind in HRI.

Output

Jupyter Notebook / Google Colab that presents the code of proposed architecture and is able to provide plug and play interaction.

a manuscript describing the proposed architecture and initial findings of the experiment

Presentations

Project Partners:

  • Sorbonne Université, Mohamed CHETOUANI
  • Örebro University (ORU), Alessandro Saffioti and Jasmin Grosinger

 

Primary Contact: Mohamed CHETOUANI, Sorbonne University

Main results of micro project:

The goal of this micro-project is to develop a cognitive architecture able to generate proactive communicative behaviors during human-robot interactions. The general idea is to provide information to humans which may help them to achieve desired outcomes, or to prevent possible undesired ones. Our work proposes a framework that generates and selects among opportunities for acting based on recognizing human intention, predicting environment changes, and reasoning about what is desirable in general. Our framework has two main modules to initiate proactive behavior; intention recognition and equilibrium maintenance.
The main achievements are:
– Integration of two systems: user intention recognition and equilibrium maintenance in a generic architecture
– Showing stability of the architecture to many users
– Reasoning mechanism and 2nd order perspective awareness
The next steps will aim to show knowledge repair, prevent outcomes of lack of knowledge and improve trustability, transparency and legibility (user study)

Contribution to the objectives of HumaneAI-net WPs

– Playground system that HumaneAI-net partners could define their interactive scenario to play with the robot’s proactivity.

-T3.3 -> Study about how to model human rationality to detect and use computationally defined human belief, goal and intention. Then, use that model to make robots proactive. Human in the loop system to support cooperative behavior of robots while sharing the environment by generating proactive communication.

-T3.1 -> Study relates robots that generate proactive communication, possible effects on human cognition and interaction strategies.

Tangible outputs