Goal-oriented autonomous decision-making for social robots via collaborative interactive inverse reinforcement learning approach.

ABSTRACT: Since the practical constraints of unknown pedestrian goal information, research on inverse reinforcement learning (IRL) applied to social robots has focused on trajectory planning based on current motion direction, other pedestrians, and obstacles. However, social robots typically have clear navigation goals, and the practicality of the aforementioned methods is debatable. Moreover, trajectory prediction at longer distances also poses significant challenges for such trajectory planning methods. Therefore, this paper proposes a goal-oriented autonomous decision-making (GO-ADM) method, which addresses the social compatibility navigation problem of robots through sequential actions at each discrete time steps, eliminating the need to predict pedestrian trajectories. Specifically, this paper

SUBMITTER: Luo M 

PROVIDER: S-EPMC12307699 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature