3,812 research outputs found
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neural network classifies these textures into material types. This approach
works effectively without the need for ambient light sources or direct contact
with materials. Furthermore, the use of a deep learning network removes the
need to handcraft the set of features for different materials. We evaluated the
performance of the system by training it to recognise 32 material types in both
indoor and outdoor environments. Our approach produced recognition accuracies
above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584
images of 17 outdoor materials. We conclude by discussing its potentials for
real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing
System
Social robot navigation tasks: combining machine learning techniques and social force model
© 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.This research was supported by the grant MDM-2016-0656 funded by MCIN/AEI / 10.13039/501100011033, the grant ROCOTRANSP PID2019-106702RB-C21 funded by MCIN/AEI/ 10.13039/501100011033 and the grant CANOPIES H2020-ICT-2020-2-101016906 funded by the European Union.Peer ReviewedPostprint (published version
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes
This paper presents two variations of a novel stochastic prediction algorithm
that enables mobile robots to accurately and robustly predict the future state
of complex dynamic scenes. The proposed algorithm uses a variational
autoencoder to predict a range of possible future states of the environment.
The algorithm takes full advantage of the motion of the robot itself, the
motion of dynamic objects, and the geometry of static objects in the scene to
improve prediction accuracy. Three simulated and real-world datasets collected
by different robot models are used to demonstrate that the proposed algorithm
is able to achieve more accurate and robust prediction performance than other
prediction algorithms. Furthermore, a predictive uncertainty-aware planner is
proposed to demonstrate the effectiveness of the proposed predictor in
simulation and real-world navigation experiments. Implementations are open
source at https://github.com/TempleRAIL/SOGMP.Comment: Accepted by 7th Annual Conference on Robot Learning (CoRL), 202
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