3 papers
cs.RO2026
Bimanual Robot Manipulation via Multi-Agent In-Context Learning
Alessio Palma, Indro Spinelli, Vignesh Prasad +4
Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf, text-only LLMs to predict ro…
cs.AI2026
Describe-Then-Act: Proactive Agent Steering via Distilled Language-Action World Models
Massimiliano Pappa, Luca Romani, Valentino Sacco +5
Deploying safety-critical agents requires anticipating the consequences of actions before they are executed. While world models offer a paradigm for this proactive foresight, curre…
cs.CV2024
Length-Aware Motion Synthesis via Latent Diffusion
Alessio Sampieri, Alessio Palma, Indro Spinelli +1
The target duration of a synthesized human motion is a critical attribute that requires modeling control over the motion dynamics and style. Speeding up an action performance is no…