2 citations · 7 across the 13 of their papers we have counts for
13 papers
FoldPath: End-to-End Object-Centric Motion Generation via Modulated Implicit Paths
Paolo Rabino, Gabriele Tiboni, Tatiana Tommasi
Object-Centric Motion Generation (OCMG) is instrumental in advancing automated manufacturing processes, particularly in domains requiring high-precision expert robotic motions, suc…
Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
Rickmer Krohn, Vignesh Prasad, Gabriele Tiboni +1
Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Learning agents struggle to learn in such…
-Level Policy Gradients for Multi-Agent Reinforcement Learning
Aryaman Reddi, Gabriele Tiboni, Jan Peters +1
Actor-critic algorithms for deep multi-agent reinforcement learning (MARL) typically employ a policy update that responds to the current strategies of other agents. While being str…
Searching on a Budget: HW-NAS with 10 Latency Probes
Francesco Capuano, Gabriele Tiboni, Niccolò Cavagnero +1
Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approximations of the post-compilation la…
Shaping Laser Pulses with Reinforcement Learning
Francesco Capuano, Davorin Peceli, Gabriele Tiboni
High Power Laser (HPL) systems operate in the attoseconds regime -- the shortest timescale ever created by humanity. HPL systems are instrumental in high-energy physics, leveraging…
MaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds
Gabriele Tiboni, Raffaello Camoriano, Tatiana Tommasi
Object-Centric Motion Generation (OCMG) plays a key role in a variety of industrial applications$\unicode{x2014}$such as robotic spray painting and welding$\unicode{x2014}$requirin…
Domain Randomization via Entropy Maximization
Gabriele Tiboni, Pascal Klink, Jan Peters +3
Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hi…
TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning
Francesco Capuano, Davorin Peceli, Gabriele Tiboni +2
High Power Laser's (HPL) optimal performance is essential for the success of a wide variety of experimental tasks related to light-matter interactions. Traditionally, HPL parameter…
Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots
Gabriele Tiboni, Andrea Protopapa, Tatiana Tommasi +1
Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability. However, the potentially infinite number of Degrees of Freedom makes their modelin…
Laser Pulse Duration Optimization With Numerical Methods
Francesco Capuano, Davorin Peceli, Gabriele Tiboni +2
In this study we explore the optimization of laser pulse duration to obtain the shortest possible pulse. We do this by employing a feedback loop between a pulse shaper and pulse du…
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…
Online vs. Offline Adaptive Domain Randomization Benchmark
Gabriele Tiboni, Karol Arndt, Giuseppe Averta +2
Physics simulators have shown great promise for conveniently learning reinforcement learning policies in safe, unconstrained environments. However, transferring the acquired knowle…
DROPO: Sim-to-Real Transfer with Offline Domain Randomization
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 manip…