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Found 8 papers, 1 papers with code
Identifying Important Sensory Feedback for Learning Locomotion Skills
Robot motor skills can be learned through deep reinforcement learning (DRL) by neural networks as state-action mappings.
A Multi-modal Garden Dataset and Hybrid 3D Dense Reconstruction Framework Based on Panoramic Stereo Images for a Trimming Robot
Next, the refined disparity maps are converted into full-view point clouds or single-view point clouds for the pose fusion module.
A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments
More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking.
Multi-expert learning of adaptive legged locomotion
Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations.
Learning Pregrasp Manipulation of Objects from Ungraspable Poses
In robotic grasping, objects are often occluded in ungraspable configurations such that no pregrasp pose can be found, eg large flat boxes on the table that can only be grasped from the side.
Robotics
Reaching, Grasping and Re-grasping: Learning Fine Coordinated Motor Skills
The performance of learned policy is evaluated on three different tasks: grasping a static target, grasping a dynamic target, and re-grasping.
Robotics
Learning Whole-body Motor Skills for Humanoids
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i. e., ankle, hip, foot tilting, and stepping strategies.
Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge
This paper presents a deep learning framework that is capable of solving partially observable locomotion tasks based on our novel interpretation of Recurrent Deterministic Policy Gradient (RDPG).
