publications

2025

1. 

   Following the Human Thread in Social Navigation

   Luca Scofano^*, Alessio Sampieri^*, Tommaso Campari^*, Valentino Sacco^*, and 3 more authors

   2025

   Abs arXiv Bib Code

   The success of collaboration between humans and robots in shared environments relies on the robot’s real-time adaptation to human motion. Specifically, in Social Navigation, the agent should be close enough to assist but ready to back up to let the human move freely, avoiding collisions. Human trajectories emerge as crucial cues in Social Navigation, but they are partially observable from the robot’s egocentric view and computationally complex to process. We present the first Social Dynamics Adaptation model (SDA) based on the robot’s state-action history to infer the social dynamics. We propose a two-stage Reinforcement Learning framework: the first learns to encode the human trajectories into social dynamics and learns a motion policy conditioned on this encoded information, the current status, and the previous action. Here, the trajectories are fully visible, i.e., assumed as privileged information. In the second stage, the trained policy operates without direct access to trajectories. Instead, the model infers the social dynamics solely from the history of previous actions and statuses in real-time. Tested on the novel Habitat 3.0 platform, SDA sets a novel state-of-the-art (SotA) performance in finding and following humans.

   @misc{scofano2025followinghumanthreadsocial,
     title = {Following the Human Thread in Social Navigation},
     author = {Scofano, Luca and Sampieri, Alessio and Campari, Tommaso and Sacco, Valentino and Spinelli, Indro and Ballan, Lamberto and Galasso, Fabio},
     year = {2025},
     eprint = {2404.11327},
     archiveprefix = {arXiv},
     primaryclass = {cs.RO},
     url = {https://arxiv.org/abs/2404.11327},
   }