arXiv subjects

Tommaso Van Der Meer

Publications and source records attributed to Tommaso Van Der Meer.

3 recordsLinked to original sources

arXiv2026

End-to-End Safe Social Navigation via Multi-Task Reinforcement Learning and Probabilistic Perception

Autonomous social navigation requires balancing efficiency, physical safety, and social compliance. Reinforcement Learning (RL) methods provide a viable and effective solution but often rely on unrealistic assumptions, such as the knowledge of humans' position and velocity. In this paper, we introduce JESSI (JAX-based E2E Safe Social Interpretable navigation), a lightweight end-to-end RL framework that maps raw LiDAR scans directly to kinematically feasible control commands. JESSI enhances safety via Dirichlet-parameterized continuous action spaces and deterministic bounding, while an integrated attention-based perception module extracts probabilistic human states for interpretable, socially aware decision-making. Through extensive simulations and real-world deployment on a differential-drive robot, we demonstrate that jointly optimizing the RL policy with a supervised perception signal in a multi-task paradigm enhances social behavior. Ultimately, JESSI is able to balance high navigation success rates and superior social behaviors compared to state-of-the-art baselines.

cs.RO↗

arXiv2026

Learning Social Robot Navigation By Sensing Human Legs

Robots navigating among pedestrians typically sense their surroundings with a 2D LiDAR mounted close to the ground. At that height, the sensor mostly sees moving legs rather than whole people, yet most learning-based navigation methods still treat pedestrians as simple shapes like circles. This paper addresses that gap with CALF (Convolutional Attention for Leg Features), an end-to-end neural architecture that combines convolutional layers, attention, and MLP to interpret leg motion directly from LiDAR scans and produce safe navigation commands. The CALF policy is trained using deep reinforcement learning algorithms within LegNav, a custom lightweight 2D simulator that combines 2D LiDAR ray tracing with a novel pedestrian gait model. The resulting policy is compared against classical and learning-based baselines in terms of navigation performance and social compliance. The approach is validated through real-world experiments via zero-shot deployment on a TurtleBot 4, yielding smooth and socially compliant trajectories. Written in JAX, the LegNav simulator enables the training of a deployment-ready CALF policy in under an hour on a single consumer GPU.

cs.RO↗

arXiv2025

A Comparative Study of Human Motion Models in Reinforcement Learning Algorithms for Social Robot Navigation

Social robot navigation is an evolving research field that aims to find efficient strategies to safely navigate dynamic environments populated by humans. A critical challenge in this domain is the accurate modeling of human motion, which directly impacts the design and evaluation of navigation algorithms. This paper presents a comparative study of two popular categories of human motion models used in social robot navigation, namely velocity-based models and force-based models. A system-theoretic representation of both model types is presented, which highlights their common feedback structure, although with different state variables. Several navigation policies based on reinforcement learning are trained and tested in various simulated environments involving pedestrian crowds modeled with these approaches. A comparative study is conducted to assess performance across multiple factors, including human motion model, navigation policy, scenario complexity and crowd density. The results highlight advantages and challenges of different approaches to modeling human behavior, as well as their role during training and testing of learning-based navigation policies. The findings offer valuable insights and guidelines for selecting appropriate human motion models when designing socially-aware robot navigation systems.

cs.HC↗