3 citations · 14 across the 8 of their papers we have counts for
15 papers · 1 filter
PUMA: Deep Metric Imitation Learning for Stable Motion Primitives
Rodrigo Pérez-Dattari, Cosimo Della Santina, Jens Kober
Imitation Learning (IL) is a powerful technique for intuitive robotic programming. However, ensuring the reliability of learned behaviors remains a challenge. In the context of rea…
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…
Two-Stage Learning of Highly Dynamic Motions with Rigid and Articulated Soft Quadrupeds
Francecso Vezzi, Jiatao Ding, Antonin Raffin +2
Controlled execution of dynamic motions in quadrupedal robots, especially those with articulated soft bodies, presents a unique set of challenges that traditional methods struggle…
Quadratic Programming-based Reference Spreading Control for Dual-Arm Robotic Manipulation with Planned Simultaneous Impacts
Jari van Steen, Gijs van den Brandt, Nathan van de Wouw +2
With the aim of further enabling the exploitation of intentional impacts in robotic manipulation, a control framework is presented that directly tackles the challenges posed by tra…
TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction
Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora +2
Predicting the future behavior of human road users is an important aspect for the development of risk-aware autonomous vehicles. While many models have been developed towards this…
Learning from Few Demonstrations with Frame-Weighted Motion Generation
Jianyong Sun, Jens Kober, Michael Gienger +1
Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to t…