by Dhimiter Pikuli, Jordan Cosio, Xavier Alameda-Pineda, Pierre-Brice Wieber, Thierry Fraichard Robotics: Science and Systems (RSS) Workshop on Unsolved Problems in Social Robot Navigation [ paper ] Navigation is one of the essential tasks in order for robots to be deployed in environments shared with humans. The problem becomes increasingly…
Xavier Alameda-Pineda was a keynote speaker at RFIAP/cAP 2024, on the topic of Learning for Companion Robots: Preparation and Adaptation.
by Ali Golmakani, Mostafa Sadeghi, Xavier Alameda-Pineda, Romain Serizel [preprint] Abstract: We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the timefrequency (TF) domain. A zero-mean complex-valued Gaussian distribution is usually assumed for the generative model, where the speech information is encoded in…
by Daniel Jost, Basavasagar Patil, Xavier Alameda-Pineda, and Chris Reinke [preprint] | [code] Abstract: Deep Neural Networks (DNNs) became the standard tool for function approximation with most of the introduced architectures being developed for high-dimensional input data. However, many real-world problems have low-dimensional inputs for which the standard Multi-Layer Perceptron…
by Hanyu Xuan, Zhiliang Wu, Jian Yang, Bo Jiang, Lei Luo, Xavier Alameda-Pineda, Yan Yan IEEE Transactions on Pattern Analysis and Machine Intelligence Abstract: By observing a scene and listening to corresponding audio cues, humans can easily recognize where the sound is. To achieve such cross-modal perception on machines, existing…
by Anand Ballou, Xavier Alameda-Pineda, and Chris Reinke Applied Intelligence [paper][code] Abstract: With the increasing presence of robots in our everyday environments, improving their social skills is of utmost importance. Nonetheless, social robotics still faces many challenges. One bottleneck is that robotic behaviors often need to be adapted, as social…
by Chris Reinke and Xavier Alameda-Pineda Transactions on Machine Learning Research [Paper][Code] Abstract. Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks. Successor features (SF) are a prominent transfer mechanism in domains where the reward function changes between tasks. They reevaluate…
by Davier Emukpere, Xavier Alameda-Pineda and Chris Reinke [Paper][code] Abstract. A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This paper investigates the integration of two frameworks for tackling those goals: episodic control and successor features. Episodic…
