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Dynamical Distance Learning for Unsupervised and Semi-Supervised Skill Discovery
Reinforcement learning requires manual specification of a reward functio...
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A Framework for Data-Driven Robotics
We present a framework for data-driven robotics that makes use of a larg...
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BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
Mobile robot navigation is typically regarded as a geometric problem, in...
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"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks via Reward Shaping
In order to learn effectively, robots must be able to extract the intang...
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Generalizing Skills with Semi-Supervised Reinforcement Learning
Deep reinforcement learning (RL) can acquire complex behaviors from low-...
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Self-supervised Learning for Precise Pick-and-place without Object Model
Flexible pick-and-place is a fundamental yet challenging task within rob...
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Efficient Bimanual Manipulation Using Learned Task Schemas
We address the problem of effectively composing skills to solve sparse-r...
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Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience
Manipulating deformable objects, such as fabric, is a long standing problem in robotics, with state estimation and control posing a significant challenge for traditional methods. In this paper, we show that it is possible to learn fabric folding skills in only an hour of self-supervised real robot experience, without human supervision or simulation. Our approach relies on fully convolutional networks and the manipulation of visual inputs to exploit learned features, allowing us to create an expressive goal-conditioned pick and place policy that can be trained efficiently with real world robot data only. Folding skills are learned with only a sparse reward function and thus do not require reward function engineering, merely an image of the goal configuration. We demonstrate our method on a set of towel-folding tasks, and show that our approach is able to discover sequential folding strategies, purely from trial-and-error. We achieve state-of-the-art results without the need for demonstrations or simulation, used in prior approaches. Videos available at: https://sites.google.com/view/learningtofold
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