45 citations · 176 across the 17 of their papers we have counts for
7 papers · 1 filter
SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning
Evelyn D'Elia, Weishu Zhan, Giulio Turrisi +5
Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics pr…
Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
Paolo Magliano, Puze Liu, Jan Peters +2
Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However, real-world deployment in ope…
Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering
Evelyn D'Elia, Paolo Maria Viceconte, Lorenzo Rapetti +5
Recent trends in humanoid robot control have successfully employed imitation learning to enable the learned generation of smooth, human-like trajectories from human data. While the…
Learning Rhythmic Trajectories with Geometric Constraints for Laser-Based Skincare Procedures
Anqing Duan, Wanli Liuchen, Jinsong Wu +3
The increasing deployment of robots has significantly enhanced the automation levels across a wide and diverse range of industries. This paper investigates the automation challenge…
A Structured Prediction Approach for Robot Imitation Learning
Anqing Duan, Iason Batzianoulis, Raffaello Camoriano +3
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observe…
PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
Gabriele Tiboni, Raffaello Camoriano, Tatiana Tommasi
Popular industrial robotic problems such as spray painting and welding require (i) conditioning on free-shape 3D objects and (ii) planning of multiple trajectories to solve the tas…
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning
Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte +4
Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on si…