Francisco Javier Acevedo-Rodríguez

Carlos Gutiérrez-Álvarez, Pablo Ríos-Navarro, Rafael Flor-Rodríguez et al.

Visual Semantic Navigation (VSN) is the ability of a robot to learn visual semantic information for navigating in unseen environments. These VSN models are typically tested in those virtual environments where they are trained, mainly using reinforcement learning based approaches. Therefore, we do not yet have an in-depth analysis of how these models would behave in the real world. In this work, we propose a new solution to integrate VSN models into real robots, so that we have true embodied agents. We also release a novel ROS-based framework for VSN, ROS4VSN, so that any VSN-model can be easily deployed in any ROS-compatible robot and tested in a real setting. Our experiments with two different robots, where we have embedded two state-of-the-art VSN agents, confirm that there is a noticeable performance difference of these VSN solutions when tested in real-world and simulation environments. We hope that this research will endeavor to provide a foundation for addressing this consequential issue, with the ultimate aim of advancing the performance and efficiency of embodied agents within authentic real-world scenarios. Code to reproduce all our experiments can be found at https://github.com/gramuah/ros4vsn.

Rafael Flor-Rodríguez, Carlos Gutiérrez-Álvarez, Francisco Javier Acevedo-Rodríguez et al.

Visual Semantic Navigation (VSN) is a fundamental problem in robotics, where an agent must navigate toward a target object in an unknown environment, mainly using visual information. Most state-of-the-art VSN models are trained in simulation environments, where rendered scenes of the real world are used, at best. These approaches typically rely on raw RGB data from the virtual scenes, which limits their ability to generalize to real-world environments due to domain adaptation issues. To tackle this problem, in this work, we propose SEMNAV, a novel approach that leverages semantic segmentation as the main visual input representation of the environment to enhance the agent's perception and decision-making capabilities. By explicitly incorporating high-level semantic information, our model learns robust navigation policies that improve generalization across unseen environments, both in simulated and real world settings. We also introduce a newly curated dataset, i.e. the SEMNAV dataset, designed for training semantic segmentation-aware navigation models like SEMNAV. Our approach is evaluated extensively in both simulated environments and with real-world robotic platforms. Experimental results demonstrate that SEMNAV outperforms existing state-of-the-art VSN models, achieving higher success rates in the Habitat 2.0 simulation environment, using the HM3D dataset. Furthermore, our real-world experiments highlight the effectiveness of semantic segmentation in mitigating the sim-to-real gap, making our model a promising solution for practical VSN-based robotic applications. We release SEMNAV dataset, code and trained models at https://github.com/gramuah/semnav

Marcos Baptista Rios, Roberto J. López-Sastre, Fabian Caba Heilbron et al.

The problem of Online Human Behaviour Recognition in untrimmed videos, aka Online Action Detection (OAD), needs to be revisited. Unlike traditional offline action detection approaches, where the evaluation metrics are clear and well established, in the OAD setting we find few works and no consensus on the evaluation protocols to be used. In this paper we introduce a novel online metric, the Instantaneous Accuracy ($IA$), that exhibits an \emph{online} nature, solving most of the limitations of the previous (offline) metrics. We conduct a thorough experimental evaluation on TVSeries dataset, comparing the performance of various baseline methods to the state of the art. Our results confirm the problems of previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario for human behaviour understanding. Code of the metric available https://github.com/gramuah/ia