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

## TL;DR

This paper introduces an interactive open-ended learning system enabling service robots to recognize multiple objects and their grasp affordances in real-time, addressing challenges of unknown objects in human-centric environments.

## Contribution

The paper presents a novel interactive learning framework combining verbal and kinesthetic teaching for object and affordance recognition in robots, improving adaptability and real-time response.

## Key findings

- High recognition accuracy demonstrated on challenging datasets.
- Effective grasp success rate achieved in real-world scenarios.
- Scalable approach suitable for diverse object categories.

## Abstract

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 recognize multiple objects and their grasp affordances concurrently. This is an important contribution in the field of service robots since no matter how extensive the training data used for batch learning, a robot might always be confronted with an unknown object when operating in human-centric environments. The paper describes the system architecture and the learning and recognition capabilities. Grasp learning associates grasp configurations (i.e., end-effector positions and orientations) to grasp affordance categories. The grasp affordance category and the grasp configuration are taught through verbal and kinesthetic teaching, respectively. A Bayesian approach is adopted for learning and recognition of object categories and an instance-based approach is used for learning and recognition of affordance categories. An extensive set of experiments has been performed to assess the performance of the proposed approach regarding recognition accuracy, scalability and grasp success rate on challenging datasets and real-world scenarios.