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Learning Navigation Behaviors End to End
A longstanding goal of behavior-based robotics is to solve high-level na...
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Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning
Mobile manipulation tasks remain one of the critical challenges for the ...
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Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning
In this paper we focus on developing a control algorithm for multi-terra...
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End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learning
In this paper, a novel deep reinforcement learning (DRL)-based method is...
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Vision-based deep execution monitoring
Execution monitor of high-level robot actions can be effectively improve...
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Robust Quadruped Jumping via Deep Reinforcement Learning
In this paper we consider a general task of jumping varying distances an...
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LBGP: Learning Based Goal Planning for Autonomous Following in Front
This paper investigates a hybrid solution which combines deep reinforcem...
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Deep Reactive Planning in Dynamic Environments
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution. While goal conditioning of policies has been studied in the RL literature, such approaches are not easily extended to cases where the robot's goal can change during execution. This is something that humans are naturally able to do. However, it is difficult for robots to learn such reflexes (i.e., to naturally respond to dynamic environments), especially when the goal location is not explicitly provided to the robot, and instead needs to be perceived through a vision sensor. In the current work, we present a method that can achieve such behavior by combining traditional kinematic planning, deep learning, and deep reinforcement learning in a synergistic fashion to generalize to arbitrary environments. We demonstrate the proposed approach for several reaching and pick-and-place tasks in simulation, as well as on a real system of a 6-DoF industrial manipulator. A video describing our work could be found <https://youtu.be/hE-Ew59GRPQ>.
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