2 citations · 3 across the 11 of their papers we have counts for
11 papers
SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception
Ziqi Zhang, Eunyeong Jin, Miguel Vasco +6
Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at inference, which is not ava…
Geometry of Uncertainty: Learning Metric Spaces for Multimodal State Estimation in RL
Alfredo Reichlin, Adriano Pacciarelli, Danica Kragic +1
Estimating the state of an environment from high-dimensional, multimodal, and noisy observations is a fundamental challenge in reinforcement learning (RL). Traditional approaches r…
Walking on the Fiber: A Simple Geometric Approximation for Bayesian Neural Networks
Alfredo Reichlin, Miguel Vasco, Danica Kragic
Bayesian Neural Networks provide a principled framework for uncertainty quantification by modeling the posterior distribution of network parameters. However, exact posterior infere…
FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functions
Daniel Marta, Simon Holk, Miguel Vasco +6
Preference-based reinforcement learning (PbRL) is a suitable approach for style adaptation of pre-trained robotic behavior: adapting the robot's policy to follow human user prefere…
FLAME: A Federated Learning Benchmark for Robotic Manipulation
Santiago Bou Betran, Alberta Longhini, Miguel Vasco +2
Recent progress in robotic manipulation has been fueled by large-scale datasets collected across diverse environments. Training robotic manipulation policies on these datasets is t…
Humans Coexist, So Must Embodied Artificial Agents
Hannah Kuehn, Joseph La Delfa, Miguel Vasco +2
This paper introduces the concept of coexistence for embodied artificial agents and argues that it is a prerequisite for long-term, in-the-wild interaction with humans. Contemporar…
Human-Aligned Image Models Improve Visual Decoding from the Brain
Nona Rajabi, Antônio H. Ribeiro, Miguel Vasco +3
Decoding visual images from brain activity has significant potential for advancing brain-computer interaction and enhancing the understanding of human perception. Recent approaches…
Can Transformers Smell Like Humans?
Farzaneh Taleb, Miguel Vasco, Antônio H. Ribeiro +2
The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory…
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks
Alfredo Reichlin, Gustaf Tegnér, Miguel Vasco +3
Given a finite set of sample points, meta-learning algorithms aim to learn an optimal adaptation strategy for new, unseen tasks. Often, this data can be ambiguous as it might belon…
Will You Participate? Exploring the Potential of Robotics Competitions on Human-centric Topics
Yuchong Zhang, Miguel Vasco, Mårten Björkman +1
This paper presents findings from an exploratory needfinding study investigating the research current status and potential participation of the competitions on the robotics communi…
Goal-Conditioned Reinforcement Learning from Sub-Optimal Data on Metric Spaces
Alfredo Reichlin, Miguel Vasco, Hang Yin +1
We study the problem of learning optimal behavior from sub-optimal datasets for goal-conditioned offline reinforcement learning under sparse rewards, invertible actions and determi…