2 citations · 3 across the 4 of their papers we have counts for
6 papers
More with LESS -- Local Scene Representations for Tactile Imaging
Zohar Rimon, Elisei Shafer, Tal Tepper +4
Tactile imaging seeks to reconstruct the internal structure of soft objects through touch sensing, with applications in medical diagnosis and robotic manipulation. Recent self-supe…
Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies
Zohar Rimon, Elisei Shafer, Tal Tepper +2
Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-su…
Task Tokens: A Flexible Approach to Adapting Behavior Foundation Models
Ron Vainshtein, Zohar Rimon, Shie Mannor +1
Recent advancements in imitation learning have led to transformer-based behavior foundation models (BFMs) that enable multi-modal, human-like control for humanoid agents. While exc…
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
Zohar Rimon, Tom Jurgenson, Orr Krupnik +2
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration. Existing meta-RL algorithms are characterized by lo…
NeRN -- Learning Neural Representations for Neural Networks
Maor Ashkenazi, Zohar Rimon, Ron Vainshtein +4
Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos. We show that, when adapted correc…
Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation Approach
Zohar Rimon, Aviv Tamar, Gilad Adler
In meta reinforcement learning (meta RL), an agent learns from a set of training tasks how to quickly solve a new task, drawn from the same task distribution. The optimal meta RL p…