Deep learning a grasp function for grasping under gripper pose uncertainty
Explore this paper's citation graph
- Type
- article
- Published
- 2016-08-07
- Cited by
- 267
- References
- 33
- Access
- Open access
- OpenAlex
- https://openalex.org/W2485911221
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16946330
Keywords
GRASP, Artificial intelligence, Computer vision, Computer science, Pose
References
- On the performance of ConvNet features for place recognition
- Leveraging big data for grasp planning
- Graspit! A versatile simulator for robotic grasping
- Functional power grasps transferred through warping and replanning
- Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX
- Real-time grasp detection using convolutional neural networks
- Fully convolutional networks for semantic segmentation
- Deep learning for detecting robotic grasps
- A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
- Generative Methods for Long-Term Place Recognition in Dynamic Scenes
- Physically-based grasp quality evaluation under uncertainty
- Pose error robust grasping from contact wrench space metrics
- DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving
- Efficient grasping from RGBD images: Learning using a new rectangle representation
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition
- Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
- Generating multi-fingered robotic grasps via deep learning
- High precision grasp pose detection in dense clutter
- Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Cited by
- Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
- 3D Simulation for Robot Arm Control with Deep Q-Learning
- Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics
- Robotic grasp detection using deep convolutional neural networks
- Learning Depth-Aware Deep Representations for Robotic Perception
- Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not
- Technology Trends and Future about Picking and Manipulation by Robots
- Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
- The Effect of Viewpoint on Grasp Detection
- Category Level Pick and Place Using Deep Reinforcement Learning
- Deep representation of industrial components using simulated images
- A cloud robot system using the dexterity network and berkeley robotics and automation as a service (Brass)
- Viewpoint selection for grasp detection
- Learning Grasping Interaction with Geometry-aware 3D Representations
- Stable pinching by controlling finger relative orientation of robotic fingers with rolling soft tips
- Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning
- Domain Randomization and Generative Models for Robotic Grasping
- Deterministic Policy Gradient Based Robotic Path Planning with Continuous Action Spaces
- A fast search algorithm based on image pyramid for robotic grasping
- Learning Deep Policies for Robot Bin Picking by Simulating Robust Grasping Sequences
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