S. Hamidreza Kasaei

Nils Keunecke, S. Hamidreza Kasaei

As a consequence of an ever-increasing number of service robots, there is a growing demand for highly accurate real-time 3D object recognition. Considering the expansion of robot applications in more complex and dynamic environments,it is evident that it is not possible to pre-program all object categories and anticipate all exceptions in advance. 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 obtained global object representation is then fed to an instance-based object category learning and recognition,where a non-expert human user exists in the learning loop and can interactively guide the process of experience acquisition by teaching new object categories, or by correcting insufficient or erroneous categories. In this work, shape information encodes the common patterns of all categories, while texture information is used to describes the appearance of each instance in detail.Multiple color space combinations and network architectures are evaluated to find the most descriptive system. Experimental results showed that the proposed network architecture out-performed the selected state-of-the-art approaches in terms of object classification accuracy and scalability. Furthermore, we performed a real robot experiment in the context of serve-a-beer scenario to show the real-time performance of the proposed approach.

S. Hamidreza Kasaei, Jorik Melsen, Floris van Beers et al.

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. 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.

Yikun Li, Lambert Schomaker, S. Hamidreza Kasaei

Grasp synthesis is one of the challenging tasks for any robot object manipulation task. In this paper, we present a new deep learning-based grasp synthesis approach for 3D objects. In particular, we propose an end-to-end 3D Convolutional Neural Network to predict the objects' graspable areas. We named our approach Res-U-Net since the architecture of the network is designed based on U-Net structure and residual network-styled blocks. It devised to plan 6-DOF grasps for any desired object, be efficient to compute and use, and be robust against varying point cloud density and Gaussian noise. We have performed extensive experiments to assess the performance of the proposed approach concerning graspable part detection, grasp success rate, and robustness to varying point cloud density and Gaussian noise. Experiments validate the promising performance of the proposed architecture in all aspects. A video showing the performance of our approach in the simulation environment can be found at: http://youtu.be/5_yAJCc8owo

S. Hamidreza Kasaei, Maryam Ghorbani, Jits Schilperoort et al.

Despite the recent success of state-of-the-art 3D object recognition approaches, service robots are frequently failed to recognize many objects in real human-centric environments. For these robots, object recognition is a challenging task due to the high demand for accurate and real-time response under changing and unpredictable environmental conditions. Most of the recent approaches use either the shape information only and ignore the role of color information or vice versa. Furthermore, they mainly utilize the $L_n$ Minkowski family functions to measure the similarity of two object views, while there are various distance measures that are applicable to compare two object views. In this paper, we explore the importance of shape information, color constancy, color spaces, and various similarity measures in open-ended 3D object recognition. Towards this goal, we extensively evaluate the performance of object recognition approaches in three different configurations, including \textit{color-only}, \textit{shape-only}, and \textit{ combinations of color and shape}, in both offline and online settings. Experimental results concerning scalability, memory usage, and object recognition performance show that all of the \textit{combinations of color and shape} yields significant improvements over the \textit{shape-only} and \textit{color-only} approaches. The underlying reason is that color information is an important feature to distinguish objects that have very similar geometric properties with different colors and vice versa. Moreover, by combining color and shape information, we demonstrate that the robot can learn new object categories from very few training examples in a real-world setting.

S. Hamidreza Kasaei

The thesis contributes in several important ways to the research area of 3D object category learning and recognition. To cope with the mentioned limitations, we look at human cognition, in particular at the fact that human beings learn to recognize object categories ceaselessly over time. This ability to refine knowledge from the set of accumulated experiences facilitates the adaptation to new environments. Inspired by this capability, we seek to create a cognitive object perception and perceptual learning architecture that can learn 3D object categories in an open-ended fashion. In this context, ``open-ended'' implies that the set of categories to be learned is not known in advance, and the training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available since the beginning of the learning process. 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. This framework integrates detection, tracking, teaching, learning, and recognition of objects. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks. The contributions presented in this thesis have been fully implemented and evaluated on different standard object and scene datasets and empirically evaluated on different robotic platforms.

S. Hamidreza Kasaei

Service robots are expected to operate effectively in human-centric environments for long periods of time. In such realistic scenarios, fine-grained object categorization is as important as basic-level object categorization. We tackle this problem by proposing an open-ended object recognition approach which concurrently learns both the object categories and the local features for encoding objects. In this work, each object is represented using a set of general latent visual topics and category-specific dictionaries. The general topics encode the common patterns of all categories, while the category-specific dictionary describes the content of each category in details. The proposed approach discovers both sets of general and specific representations in an unsupervised fashion and updates them incrementally using new object views. Experimental results show that our approach yields significant improvements over the previous state-of-the-art approaches concerning scalability and object classification performance. Moreover, our approach demonstrates the capability of learning from very few training examples in a real-world setting. Regarding computation time, the best result was obtained with a Bag-of-Words method followed by a variant of the Latent Dirichlet Allocation approach.

S. Hamidreza Kasaei

Nowadays service robots are leaving the structured and completely known environments and entering human-centric settings. For these robots, object perception and grasping are two challenging tasks due to the high demand for accurate and real-time responses. Although many problems have already been understood and solved successfully, many challenges still remain. Open-ended learning is one of these challenges waiting for many improvements. Cognitive science revealed that humans learn to recognize object categories and grasp affordances ceaselessly over time. This ability allows adapting to new environments by enhancing their knowledge from the accumulation of experiences and the conceptualization of new object categories. Inspired by this, an autonomous robot must have the ability to process visual information and conduct learning and recognition tasks in an open-ended fashion. In this context, "open-ended" implies that the set of object categories to be learned is not known in advance, and the training instances are extracted from online experiences of a robot, and become gradually available over time, rather than being completely available at the beginning of the learning process. In my research, I mainly focus on interactive open-ended learning approaches to recognize multiple objects and their grasp affordances concurrently. In particular, I try to address the following research questions: (i) What is the importance of open-ended learning for autonomous robots? (ii) How robots could learn incrementally from their own experiences as well as from interaction with humans? (iii) What are the limitations of Deep Learning approaches to be used in an open-ended manner? (iv) How to evaluate open-ended learning approaches and what are the right metrics to do so?

S. Hamidreza Kasaei, Nima Shafii, Luis Seabra Lopes et al.

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.