1 citations · 1 across the 3 of their papers we have counts for
8 papers
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…