Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
This article presents a robotic pick-and-place system that is capable of grasping and
recognizing both known and novel objects in cluttered environments. The key new feature of …
recognizing both known and novel objects in cluttered environments. The key new feature of …
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Current model free learning-based robot grasping approaches exploit human-labeled
datasets for training the models. However, there are two problems with such a …
datasets for training the models. However, there are two problems with such a …
Data-driven grasp synthesis—a survey
We review the work on data-driven grasp synthesis and the methodologies for sampling and
ranking candidate grasps. We divide the approaches into three groups based on whether …
ranking candidate grasps. We divide the approaches into three groups based on whether …
Leveraging big data for grasp planning
We propose a new large-scale database containing grasps that are applied to a large set of
objects from numerous categories. These grasps are generated in simulation and are …
objects from numerous categories. These grasps are generated in simulation and are …
Domain randomization and generative models for robotic grasping
J Tobin, L Biewald, R Duan… - 2018 IEEE/RSJ …, 2018 - ieeexplore.ieee.org
Deep learning-based robotic grasping has made significant progress thanks to algorithmic
improvements and increased data availability. However, state-of-the-art models are often …
improvements and increased data availability. However, state-of-the-art models are often …
The curious robot: Learning visual representations via physical interactions
What is the right supervisory signal to train visual representations? Current approaches in
computer vision use category labels from datasets such as ImageNet to train ConvNets …
computer vision use category labels from datasets such as ImageNet to train ConvNets …
Reinforcement learning with sequences of motion primitives for robust manipulation
Physical contact events often allow a natural decomposition of manipulation tasks into action
phases and subgoals. Within the motion primitive paradigm, each action phase corresponds …
phases and subgoals. Within the motion primitive paradigm, each action phase corresponds …
One-shot learning and generation of dexterous grasps for novel objects
This paper presents a method for one-shot learning of dexterous grasps and grasp
generation for novel objects. A model of each grasp type is learned from a single kinesthetic …
generation for novel objects. A model of each grasp type is learned from a single kinesthetic …
Minimum volume bounding box decomposition for shape approximation in robot grasping
Thinking about intelligent robots involves consideration of how such systems can be
enabled to perceive, interpret and act in arbitrary and dynamic environments. While sensor …
enabled to perceive, interpret and act in arbitrary and dynamic environments. While sensor …
Development of robotic bin picking platform with cluttered objects using human guidance and convolutional neural network (CNN)
Industrial robots have been utilized for factory automation due to their high repeatability.
Along with the development of visual servo and machine learning techniques, various vision …
Along with the development of visual servo and machine learning techniques, various vision …