5 papers
Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models
Saraswati Soedarmadji, Yunyue Wei, Chen Zhang +2
Discovering effective reward functions remains a fundamental challenge in motor control of high-dimensional musculoskeletal systems. While humans can describe movement goals explic…
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems
Yunyue Wei, Zeji Yi, Hongda Li +2
Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such…
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
Yunyue Wei, Vincent Zhuang, Saraswati Soedarmadji +1
Bayesian optimization is an effective technique for black-box optimization, but its applicability is typically limited to low-dimensional and small-budget problems due to the cubic…
Population Transformer: Learning Population-level Representations of Neural Activity
Geeling Chau, Christopher Wang, Sabera Talukder +5
We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with ne…
Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
Connor Lee, Saraswati Soedarmadji, Matthew Anderson +2
We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products…