5 papers · 1 filter
Frequency-based View Selection in Gaussian Splatting Reconstruction
Monica M. Q. Li, Pierre-Yves Lajoie, Giovanni Beltrame
Three-dimensional reconstruction is a fundamental problem in robotics perception. We examine the problem of active view selection to perform 3D Gaussian Splatting reconstructions w…
Variable Time Step Reinforcement Learning for Robotic Applications
Dong Wang, Giovanni Beltrame
Traditional reinforcement learning (RL) generates discrete control policies, assigning one action per cycle. These policies are usually implemented as in a fixed-frequency control…
MOSEAC: Streamlined Variable Time Step Reinforcement Learning
Dong Wang, Giovanni Beltrame
Traditional reinforcement learning (RL) methods typically employ a fixed control loop, where each cycle corresponds to an action. This rigidity poses challenges in practical applic…
Reinforcement Learning with Elastic Time Steps
Dong Wang, Giovanni Beltrame
Traditional Reinforcement Learning (RL) policies are typically implemented with fixed control rates, often disregarding the impact of control rate selection. This can lead to ineff…
Deployable Reinforcement Learning with Variable Control Rate
Dong Wang, Giovanni Beltrame
Deploying controllers trained with Reinforcement Learning (RL) on real robots can be challenging: RL relies on agents' policies being modeled as Markov Decision Processes (MDPs), w…