Online Motion Concept Learning: A Novel Algorithm for Sample-Efficient Learning and Recognition of Human Actions. - Miguel Vasco; Francisco S. Melo; David Martins de Matos; Ana Paiva 001; Tetsunari Inamura (AAMAS, 2019)

Learning Multimodal Representations for Sample-efficient Recognition of Human Actions. - Miguel Vasco; Francisco S. Melo; David Martins de Matos; Ana Paiva 001; Tetsunari Inamura (IROS, 2019)

Playing Games in the Dark: An Approach for Cross-Modality Transfer in Reinforcement Learning. - Rui Silva; Miguel Vasco; Francisco S. Melo; Ana Paiva 001; Manuela Veloso (AAMAS, 2020)

Multimodal Representation Learning for Robotic Cross-Modality Policy Transfer. - Miguel Vasco (AAMAS, 2020)

Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios. - Silvia Tulli; Marta Couto; Miguel Vasco; Elmira Yadollahi; Francisco S. Melo; Ana Paiva 001 (ICSR, 2020)

MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning. - Miguel Vasco; Francisco S. Melo; Ana Paiva 001 (CoRR, 2020)

Exploiting Symmetry in Human Robot-Assisted Dressing Using Reinforcement Learning. - Pedro Ildefonso; Pedro Remédios; Rui Silva; Miguel Vasco; Francisco S. Melo; Ana Paiva 001; Manuela Veloso (EPIA, 2021)

How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents. - Miguel Vasco; Hang Yin 001; Francisco S. Melo; Ana Paiva 001 (CoRR, 2021)

How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents. - Miguel Vasco; Hang Yin 001; Francisco S. Melo; Ana Paiva 001 (AAMAS, 2022)

Geometric Multimodal Contrastive Representation Learning. - Petra Poklukar; Miguel Vasco; Hang Yin 001; Francisco S. Melo; Ana Paiva 001; Danica Kragic (ICML, 2022)

Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories. - Fábio Vital; Miguel Vasco; Alberto Sardinha; Francisco S. Melo (IROS, 2022)

GMC - Geometric Multimodal Contrastive Representation Learning. - Petra Poklukar; Miguel Vasco; Hang Yin 001; Francisco S. Melo; Ana Paiva 001; Danica Kragic (CoRR, 2022)

Leveraging hierarchy in multimodal generative models for effective cross-modality inference. - Miguel Vasco; Hang Yin 001; Francisco S. Melo; Ana Paiva 001 (Neural Networks, 2022)

Pre-training with Augmentations for Efficient Transfer in Model-Based Reinforcement Learning. - Bernardo Esteves; Miguel Vasco; Francisco S. Melo (EPIA (1), 2023)

Mental Face Image Retrieval Based on a Closed-Loop Brain-Computer Interface. - Nona Rajabi; Charles Chernik; Alfredo Reichlin; Farzaneh Taleb; Miguel Vasco; Ali Ghadirzadeh; Mårten Björkman; Danica Kragic (HCI (9), 2023)

Detecting the Intention of Object Handover in Human-Robot Collaborations: An EEG Study. - Nona Rajabi; Parag Khanna; Sümeyra Demir Kanik; Elmira Yadollahi; Miguel Vasco; Mårten Björkman; Christian Smith; Danica Kragic (RO-MAN, 2023)

Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning. - Pedro P. Santos; Diogo S. Carvalho; Miguel Vasco; Alberto Sardinha; Pedro A. Santos 001; Ana Paiva 001; Francisco S. Melo (AAMAS, 2024)

Challenging Deep Learning Methods for EEG Signal Denoising under Data Corruption. - Farzaneh Taleb; Miguel Vasco; Nona Rajabi; Mårten Björkman; Danica Kragic (EMBC, 2024)

Will You Participate? Exploring the Potential of Robotics Competitions on Human-Centric Topics. - Yuchong Zhang 001; Miguel Vasco; Mårten Björkman; Danica Kragic (HCI (2), 2024)

NeuralSolver: Learning Algorithms For Consistent and Efficient Extrapolation Across General Tasks. - Bernardo Esteves; Miguel Vasco; Francisco S. Melo (NeurIPS, 2024)

Can Transformers Smell Like Humans? - Farzaneh Taleb; Miguel Vasco; Antônio H. Ribeiro; Mårten Björkman; Danica Kragic (NeurIPS, 2024)

A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo. - Miguel Vasco; Takuma Seno; Kenta Kawamoto; Kaushik Subramanian; Peter R. Wurman; Peter Stone 001 (RLJ, 2024)

Goal-Conditioned Offline Reinforcement Learning via Metric Learning. - Alfredo Reichlin; Miguel Vasco; Hang Yin 001; Danica Kragic (CoRR, 2024)

NeuralThink: Algorithm Synthesis that Extrapolates in General Tasks. - Bernardo Esteves; Miguel Vasco; Francisco S. Melo (CoRR, 2024)

Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks. - Alfredo Reichlin; Gustaf Tegnér; Miguel Vasco; Hang Yin 001; Mårten Björkman; Danica Kragic (Trans. Mach. Learn. Res., 2024)

Deep Learning Amplified Early Stopping Bias: Overestimating Performance on Small Datasets. - Nona Rajabi; Antônio H. Ribeiro; Miguel Vasco; Danica Kragic (ICASSP, 2025)

Walking on the Fiber: A Simple Geometric Approximation for Bayesian Neural Networks. - Alfredo Reichlin; Miguel Vasco; Danica Kragic (Trans. Mach. Learn. Res., 2025)

Human-Aligned Image Models Improve Visual Decoding from the Brain. - Nona Rajabi; Antônio H. Ribeiro; Miguel Vasco; Farzaneh Taleb; Mårten Björkman; Danica Kragic (CoRR, 2025)

Humans Co-exist, So Must Embodied Artificial Agents. - Hannah Kuehn; Joseph La Delfa; Miguel Vasco; Danica Kragic; Iolanda Leite (CoRR, 2025)

FLAME: A Federated Learning Benchmark for Robotic Manipulation. - Santiago Bou Betran; Alberta Longhini; Miguel Vasco; Yuchong Zhang 001; Danica Kragic (CoRR, 2025)

FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functions. - Daniel Marta; Simon Holk; Miguel Vasco; Jens Lundell; Timon Homberger; Finn Busch; Olov Andersson; Danica Kragic; Iolanda Leite (CoRR, 2025)