1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
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…
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…
-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…
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…