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LearningforGraspingandManipulationI

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Paper List

  • FSG-Net: A Deep Learning Model for Semantic Robot Grasping through Few-Shot Learning [Abstract]

  • Learning Pre-Grasp Manipulation of Flat Objects in Cluttered Environments Using Sliding Primitives [Abstract]

  • Learning Category-Level Manipulation Tasks from Point Clouds with Dynamic Graph CNNs [Abstract]

  • Neural Grasp Distance Fields for Robot Manipulation [Abstract]

  • Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers [Abstract]

  • Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation [Abstract]

  • Practical Visual Deep Imitation Learning Via Task-Level Domain Consistency [Abstract]

  • SEIL: Simulation-Augmented Equivariant Imitation Learning [Abstract]

  • Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture [Abstract]

  • Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation [Abstract]

  • CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation [Abstract]

  • NIFT: Neural Interaction Field and Template for Object Manipulation [Abstract]