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Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
In order to robustly execute a task under environmental uncertainty, a r...
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Learning Topological Motion Primitives for Knot Planning
In this paper, we approach the challenging problem of motion planning fo...
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Sample Efficient Reinforcement Learning through Learning from Demonstrations in Minecraft
Sample inefficiency of deep reinforcement learning methods is a major ob...
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Learning Bayes Filter Models for Tactile Localization
Localizing and tracking the pose of robotic grippers are necessary skill...
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Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changi...
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Segmenting and Sequencing of Compliant Motions
This paper proposes an approach for segmenting a task consisting of comp...
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Fast and Continuous Foothold Adaptation for Dynamic Locomotion through Convolutional Neural Networks
Legged robots can outperform wheeled machines for most navigation tasks ...
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Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning from demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.
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