245 citations · 837 across the 21 of their papers we have counts for
4 papers · 1 filter
The Reality Gap in Robotics: Challenges, Solutions, and Best Practices
Elie Aljalbout, Jiaxu Xing, Angel Romero +9
Machine learning has facilitated significant advancements across various robotics domains, including navigation, locomotion, and manipulation. Many such achievements have been driv…
Learning Acrobatic Flight from Preferences
Colin Merk, Ismail Geles, Jiaxu Xing +3
Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where…
Accelerating Model-Based Reinforcement Learning with State-Space World Models
Maria Krinner, Elie Aljalbout, Angel Romero +1
Reinforcement learning (RL) is a powerful approach for robot learning. However, model-free RL (MFRL) requires a large number of environment interactions to learn successful control…
Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight
Angel Romero, Ashwin Shenai, Ismail Geles +2
Autonomous drone racing has risen as a challenging robotic benchmark for testing the limits of learning, perception, planning, and control. Expert human pilots are able to fly a dr…