Valentino Sacco

Edificio E, viale Regina Elena 295, 00161

Rome, Italy

I am a Computer Science PhD Candidate at Sapienza University of Rome, where I am part of the Perception and Intelligence Laboratory (PINlab), supervised by Professor Fabio Galasso.

My research focuses on Socially Aware Robotics and Human-Robot Interaction, with a background in Computer Vision and Deep Reinforcement Learning.

Previously, I led a small development team for a startup company and worked as a web development consultant. I have since focused my career on research and am open to opportunities for research internships, collaborative visits, and industry or academic projects in my areas of interest.

news

Oct 02, 2025	I am soon entering the third and final year of my PhD studies! After my research visit to Panasonic R&D in Silicon Valley, I am especially excited about opportunities to collaborate with industry alongside my academic work.
Apr 24, 2025	In the next few days I will be attending ICLR 2025 in Singapore, where I will present our spotlight paper: Following the Human Thread in Social Navigation.
Feb 15, 2025	I am visiting Panasonic R&D North America in California for a research stay, where I will also be collaborating with UC Berkeley. I’m thrilled about this opportunity!

selected publications

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}, }