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Found 7 papers, 1 papers with code
Open-Ended Fine-Grained 3D Object Categorization by Combining Shape and Texture Features in Multiple Colorspaces
Therefore, robots should have the functionality to learn about new object categories in an open-ended fashion while working in the environment. Towards this goal, we propose a deep transfer learning approach to generate a scale- and pose-invariant object representation by considering shape and texture information in multiple colorspaces.
The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation
In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming.
Investigating the Importance of Shape Features, Color Constancy, Color Spaces and Similarity Measures in Open-Ended 3D Object Recognition
In this paper, we explore the importance of shape information, color constancy, color spaces, and various similarity measures in open-ended 3D object recognition.
Learning to Grasp 3D Objects using Deep Residual U-Nets
Affordance detection is one of the challenging tasks in robotics because it must predict the grasp configuration for the object of interest in real-time to enable the robot to interact with the environment.
Robotics
Interactive Open-Ended Learning for 3D Object Recognition
In particular, this architecture provides perception capabilities that will allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform complex tasks.
Look Further to Recognize Better: Learning Shared Topics and Category-Specific Dictionaries for Open-Ended 3D Object Recognition
In this work, each object is represented using a set of general latent visual topics and category-specific dictionaries.
Object Perception and Grasping in Open-Ended Domains
Open-ended learning is one of these challenges waiting for many improvements.
Robotics
