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Found 3 papers, 2 papers with code
Implicit Behavioral Cloning
We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models.
Forward-Backward Reinforcement Learning
If we relax this one restriction and endow the agent with knowledge of the reward function, and in particular of the goal, we can leverage backwards induction to accelerate training.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.
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