Domain Randomization via Entropy Maximization

no code implementations • 3 Nov 2023 • Gabriele Tiboni, Pascal Klink, Jan Peters, Tatiana Tommasi, Carlo D'Eramo, Georgia Chalvatzaki

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL).

Reinforcement Learning (RL)

TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning

1 code implementation • 20 Apr 2023 • Francesco Capuano, Davorin Peceli, Gabriele Tiboni, Raffaello Camoriano, Bedřich Rus

Furthermore, DRL aims to find an optimal control policy rather than a static parameter configuration, particularly suitable for dynamic processes involving sequential decision-making.

Decision Making reinforcement-learning

Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots

no code implementations • 7 Mar 2023 • Gabriele Tiboni, Andrea Protopapa, Tatiana Tommasi, Giuseppe Averta

Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability.

reinforcement-learning Reinforcement Learning (RL)

PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting

no code implementations • 13 Nov 2022 • 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 task.

Online vs. Offline Adaptive Domain Randomization Benchmark

1 code implementation • 29 Jun 2022 • Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tommasi

However, transferring the acquired knowledge to the real world can be challenging due to the reality gap.

DROPO: Sim-to-Real Transfer with Offline Domain Randomization

1 code implementation • 20 Jan 2022 • Gabriele Tiboni, Karol Arndt, Ville Kyrki

In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult.

Reinforcement Learning (RL)