2 citations · 5 across the 5 of their papers we have counts for
5 papers
Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning
Ben Zandonati, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Humans can learn a new manipulation task from one or two demonstrations and then perform it in a new room, with new objects, under new constraints. Modern robot imitation learning,…
Hierarchical Vision-Language Planning for Multi-Step Humanoid Manipulation
André Schakkal, Ben Zandonati, Zhutian Yang +1
Enabling humanoid robots to reliably execute complex multi-step manipulation tasks is crucial for their effective deployment in industrial and household environments. This paper pr…
Investigating Vision Foundational Models for Tactile Representation Learning
Ben Zandonati, Ruohan Wang, Ruihan Gao +1
Tactile representation learning (TRL) equips robots with the ability to leverage touch information, boosting performance in tasks such as environment perception and object manipula…
Towards Optimal Compression: Joint Pruning and Quantization
Ben Zandonati, Glenn Bucagu, Adrian Alan Pol +3
Model compression is instrumental in optimizing deep neural network inference on resource-constrained hardware. The prevailing methods for network compression, namely quantization…
FIT: A Metric for Model Sensitivity
Ben Zandonati, Adrian Alan Pol, Maurizio Pierini +2
Model compression is vital to the deployment of deep learning on edge devices. Low precision representations, achieved via quantization of weights and activations, can reduce infer…