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Found 5 papers, 3 papers with code
Pick and Place Without Geometric Object Models
Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation.
Robotics
Grasp Pose Detection in Point Clouds
Many grasp detection methods achieve grasp success rates (grasp successes as a fraction of the total number of grasp attempts) between 75% and 95% for novel objects presented in isolation or in light clutter.
Robotics
Learning a visuomotor controller for real world robotic grasping using simulated depth images
This paper proposes an approach to learning a closed-loop controller for robotic grasping that dynamically guides the gripper to the object.
High precision grasp pose detection in dense clutter
Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network.
Robotics
Using Geometry to Detect Grasps in 3D Point Clouds
Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter.
Robotics
