Sim2Real Manipulation on Unknown Objects with Tactile-based Reinforcement Learning
arxiv(2024)
摘要
Using tactile sensors for manipulation remains one of the most challenging
problems in robotics. At the heart of these challenges is generalization: How
can we train a tactile-based policy that can manipulate unseen and diverse
objects? In this paper, we propose to perform Reinforcement Learning with only
visual tactile sensing inputs on diverse objects in a physical simulator. By
training with diverse objects in simulation, it enables the policy to
generalize to unseen objects. However, leveraging simulation introduces the
Sim2Real transfer problem. To mitigate this problem, we study different tactile
representations and evaluate how each affects real-robot manipulation results
after transfer. We conduct our experiments on diverse real-world objects and
show significant improvements over baselines for the pivoting task. Our project
page is available at https://tactilerl.github.io/.
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