Two ways to make your robot proactive: Reasoning about human intentions or reasoning about possible futures

Abstract

Robots sharing their space with humans need to be proactive to be helpful. Proactive robots can act on their own initiatives in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecast. In this article, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to consider a broader variety of aspects that are required for proactivity.

Enhancing Robotic Perception through Synchronized Simulation and Physical Common-Sense Reasoning

“…An additional promising avenue of research focuses on interpreting human activities [31]. One possibility could be implemented by combining the retargeting of the perceived body into the robot's geometry and the retrieval of previous sensorimotor episodes in similar situations, such as grasping a box.…”

A Bayesian Framework for Learning Proactive Robot Behaviour in Assistive Tasks

“…Building on the results of these works, they then proposed a framework that formalises proactivity through reasoning about the actions the robot can perform, the state of the environment, and the user's state, intentions, and preferences (Grosinger et al, 2019). In addition, based on the latter work, it was defined a new architecture combining Equilibrium Maintenance with "human intention recognition and reasoning" to create a proactive robot that considered both human intentions and temporal predictions (Buyukgoz et al, 2022). While those works focused on anticipatory ways to make temporal projections to predict undesirable future states, they did not predict the temporal time to act.…”