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Luís Seabra Lopes

7 accepted papers

2019

Interactive Open-Ended Object, Affordance and Grasp Learning for Robotic Manipulation

ICRA 2019poster

Service robots are expected to autonomously and efficiently work in human-centric environments. For this type of robots, object perception and manipulation are challenging tasks due to need for accurate and real-time response. This paper presents an interactive open-ended learning approach to recogn…

Cited by 24SourceScholar
2019

Learning the Scope of Applicability for Task Planning Knowledge in Experience-Based Planning Domains

IROS 2019poster

Experience-based planning domains (EBPDs) have been proposed to improve problem solving by learning from experience. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA),…

Cited by 6SourceScholar
2016

An orthographic descriptor for 3D object learning and recognition

IROS 2016poster

Object representation is one of the most challenging tasks in robotics because it must provide reliable information in real-time to enable the robot to physically interact with the objects in its environment. To ensure reliability, a global object descriptor must be computed based on a unique and re…

Cited by 14SourceScholar
2016

Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition

NeurIPS 2016poster

Most robots lack the ability to learn new objects from past experiences. To migrate a robot to a new environment one must often completely re-generate the knowledge- base that it is running with. Since in open-ended domains the set of categories to be learned is not predefined, it is not feasible to…

Cited by 21SourcePDFScholar
2016

Learning to grasp familiar objects using object view recognition and template matching

IROS 2016poster

Robots are still not able to grasp all unforeseen objects. Finding a proper grasp configuration, i.e. the position and orientation of the arm relative to the object, is still challenging. One approach for grasping unforeseen objects is to recognize an appropriate grasp configuration from previous gr…

Cited by 24SourceScholar
2015

Concurrent learning of visual codebooks and object categories in open-ended domains

IROS 2015poster

In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, vis…

Cited by 25SourceScholar