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Found 5 papers, 0 papers with code
Neural Packing: from Visual Sensing to Reinforcement Learning
We present a novel learning framework to solve the transport-and-packing (TAP) problem in 3D.
NIFT: Neural Interaction Field and Template for Object Manipulation
Given a few object manipulation demos, NIFT guides the generation of the interaction imitation for a new object instance by matching the Neural Interaction Template (NIT) extracted from the demos in the target Neural Interaction Field (NIF) defined for the new object.
Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction
To resolve the sample efficiency issue in learning the high-dimensional and complex control of dexterous grasping, we propose an effective representation of grasping state characterizing the spatial interaction between the gripper and the target object.
Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning
Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set.
TAP-Net: Transport-and-Pack using Reinforcement Learning
Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search.
