103 citations · 109 across the 5 of their papers we have counts for
6 papers · 1 filter
Fast Non-Parametric Heteroscedastic Imitation Learning With Geometric Priors
Maximilian Mühlbauer, Arne Sachtler, Markus Knauer +4
When learning probabilistic policies from human demonstrations, data-efficient learning and fast adaptations to new scenarios are key requirements. One popular way to achieve intui…
Learning Geometry-Aware Virtual Fixtures From Sparse Demonstrations
Maximilian Mühlbauer, Marcella Piacentino, Raffaella Mancino +10
In many teleoperation applications, collecting a large number of demonstrations as required for traditional probabilistic learning from demonstration (LfD) approaches may not be fe…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks
Antonin Raffin, Olivier Sigaud, Jens Kober +3
In search of a simple baseline for Deep Reinforcement Learning in locomotion tasks, we propose a model-free open-loop strategy. By leveraging prior knowledge and the elegance of si…
A Non-parametric Skill Representation with Soft Null Space Projectors for Fast Generalization
João Silvério, Yanlong Huang
Over the last two decades, the robotics community witnessed the emergence of various motion representations that have been used extensively, particularly in behavorial cloning, to…
Learning to Exploit Elastic Actuators for Quadruped Locomotion
Antonin Raffin, Daniel Seidel, Jens Kober +3
Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design. While previous work has focused on…