319 citations · 614 across the 14 of their papers we have counts for
3 papers · 1 filter
GoSafe: Globally Optimal Safe Robot Learning
Dominik Baumann, Alonso Marco, Matteo Turchetta +1
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage. SafeOpt is an efficient Bayesian opti…
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
Matteo Turchetta, Andreas Krause, Sebastian Trimpe
In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment. In real-world a…
Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations
Mark Pfeiffer, Samarth Shukla, Matteo Turchetta +4
This work presents a case study of a learning-based approach for target driven map-less navigation. The underlying navigation model is an end-to-end neural network which is trained…