Osher Azulay

I’m a passionate roboticist pursuing my PhD studies at the ROB-TAU Robotics Lab within the MechEng Dept. at Tel-Aviv University. My research centers primarily on understanding how robots learn to sense their environment, with a focus on robotic manipulations, intelligent decision-making, and tactile sensing.

Find my latest work here, feel free to contact me for any questions!.

Email  /  CV  /  Scholar  /  LinkedIn  /  Github

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ag Augmenting Tactile Simulators with Real-like and Zero-Shot Capabilities
Osher Azulay*, Alon Mizrahi*, Nimrod Curtis* and Avishai Sintov.
Preprint.
paper / code

Tackling the sim-to-real problem for high resolution 3D round sensors using bi-directional Generative Adversarial Networks.
allsight AllSight: A Low-Cost and High-Resolution Round Tactile Sensor with Zero-Shot Learning Capability
Osher Azulay, Nimrod Curtis, Rotem Sokolovsky, Guy Levitski, Daniel Slomovik, Guy Lilling and Avishai Sintov.
IEEE RA-L & ICRA, 2024.
paper / video / code

Introducing AllSight, an optical tactile sensor with a round 3D structure designed for robotic inhand manipulation tasks
hapticrl Haptic-Based and SE(3)-Aware Object Insertion Using Compliant Hands
Osher Azulay, Max Monastirsky and Avishai Sintov.
IEEE RA-L & ICRA, 2023.
paper / video

Exploring complaint hands characteristics for object insertion using haptic-based residual RL.
throw Learning to Throw With a Handful of Samples Using Decision Transformers
Max Monastirsky, Osher Azulay and Avishai Sintov.
IEEE RA-L & IROS, 2023.
paper / video

Exploring the use of Decision Transformers for throwing and their ability for sim2real policy transfer.
hapticmpc Learning Haptic-based Object Pose Estimation for In-hand Manipulation Control with Underactuated Robotic Hands
Osher Azulay, Inbar Meir and Avishai Sintov.
IEEE Transactions on Haptics, 2022.
paper / video / code

In-hand object pose estimation and manipulation using Model Predictive Control.
ops Open-Sourcing Generative Models for Data-driven Robot Simulations
Eran Bamani, Osher Azulay, Anton Gurevich, and Avishai Sintov.
Data-Centric AI workshop, NeurIPS, 2021
paper / oral

Exploring the possibility of investing the recorded data in a generative model rather than directly to a regression model for real-robot applications.
komodo Wheel Loader Scooping Controller Using Deep Reinforcement Learning
Osher Azulay and Amir Shapiro.
IEEE Access, 2021
paper / video / code

A deep reinforcement learning-based controller for an unmanned ground vehicle with a custom-built scooping mechanism.

Talks, Honor &
Recognition
  • Nehemia Levtzion Scholarship for PhD excellence.
  • KLA Scholarships for PhD excellence, 2022.
  • (TLV MechEng Dept.) Dean’s Excellence in Teaching award, 2022.
  • Invited to talk @ Annual meeting for Motion Control and Automation, Expo Tel-Aviv, 2021.
  • (BGU MechEng Dept.) Honor List: 2017-2018, 2018-2019.

Template from source code.