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Validate on Sim, Detect on Real -- Model Selection for Domain Randomization
Gal Leibovich, Guy Jacob, Shadi Endrawis +2
A practical approach to learning robot skills, often termed sim2real, is to train control policies in simulation and then deploy them on a real robot. Popular techniques to improve…
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability
Aviv Tamar, Daniel Soudry, Ev Zisselman
In the Bayesian reinforcement learning (RL) setting, a prior distribution over the unknown problem parameters -- the rewards and transitions -- is assumed, and a policy that optimi…
Efficient Self-Supervised Data Collection for Offline Robot Learning
Shadi Endrawis, Gal Leibovich, Guy Jacob +2
A practical approach to robot reinforcement learning is to first collect a large batch of real or simulated robot interaction data, using some data collection policy, and then lear…
Unsupervised Feature Learning for Manipulation with Contrastive Domain Randomization
Carmel Rabinovitz, Niko Grupen, Aviv Tamar
Robotic tasks such as manipulation with visual inputs require image features that capture the physical properties of the scene, e.g., the position and configuration of objects. Rec…