Above are papers related to the following paper: [Paper_ID] Paper #245288. 	[Title] Learning the Peg-into-Hole Assembly Operation with a Connectionist Reinforcement Technique [Abstract] The paper presents a learning controller that is capable of increasing insertion speed during consecutive peg-into-hole operations, without increasing the contact force level. Our aim is to find a better relationship between measured forces and the controlled velocity, without using a complicated (human generated) model. We followed a connectionist approach. Two learning phases are distinguished. First the learning controller is trained (or initialised) in a supervised way by a suboptimal task frame controller. Then a reinforcement learning phase follows. The controller consists of two networks: (1) the policy network and (2) the exploration network. On-line robotic exploration plays a crucial role in obtaining a better policy. Optionally, this architecture can be extended with a third network: the reinforcement network. The learning controller is implemented on a CAD-based contact force simulator. In contrast with most other related work, the experiments are simulated in 3D with 6 degrees of freedom. Performance of a peg-into-hole task is measured in insertion time and average/maximum force level. The fact that a better performance can be obtained in this way, demonstrates the importance of model-free learning techniques for repetitive robotic assembly tasks. The paper presents the approach and simulation results. Keywords: robotic assembly, peg-into-hole, artificial neural networks, reinforcement learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1122460. 	[Title] Learning Controllers from Examples  a motivation for searching alternative, empirical techniques for generating controllers. [Abstract] Today there is a great interest in discovering methods that allow a faster design and development of real-time control software. Control theory helps when linear controllers have to be developed but it does not support the generation In this paper, it is discussed how Machine Learning has been applied to the Function, and Locally Receptive Field Function Approximators. Three integrated learning algorithms, two of which are original, are described and then tried on two experimental test cases. The first test case is provided by an industrial robot KUKA IR-361 engaged into the "peg-into-hole" task, while the second is a classical prediction task on the Mackey-Glass chaotic series. From the experimental comparison, it appears that both Fuzzy Controllers and RBFNs synthesised from examples are excellent approximators, and that they can be even more accurate than MLPs. of non-linear controllers, which in many cases (such as in compliant motion control) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #910. 	[Title] Learning networks for face analysis and synthesis [Abstract] This paper presents an overview of the face-related projects in our group. The unifying theme underlying our work is the use of example-based learning methods for both analyzing and synthesizing face images. We label the example face images (and for the problem of face detection, "near miss" faces as well) with descriptive parameters for pose, expression, identity, and face vs. non-face. Then, by using example-based learning techniques, we develop networks for performing analysis tasks such as pose and expression estimation, face recognition, and face detection in cluttered scenes. In addition to these analysis applications, we show how the example-based technique can also be used as a novel method for image synthesis that is for computer graphics. Please summarise all the given information.

Paper #245288 has the title `Learning the Peg-into-Hole Assembly Operation with a Connectionist Reinforcement Technique  
` and the abstract `The paper presents a learning controller that is capable of increasing insertion speed during consecutive peg-into-hole operations, without increasing the contact force level. Our aim is to find a better relationship between measured forces and the controlled velocity, without using a complicated (human generated) model. We followed a connectionist approach. Two learning phases are distinguished. First the learning controller is trained (or initialised) in a supervised way by a suboptimal task frame controller. Then a reinforcement learning phase follows. The controller consists of two networks: (1) the policy network and (2) the exploration network. On-line robotic exploration plays a crucial role in obtaining a better policy. Optionally, this architecture can be extended with a third network: the reinforcement network. The learning controller is implemented on a CAD-based contact force simulator. In contrast with most other related work, the experiments are simulated in 3D with 6 degrees of freedom. Performance of a peg-into-hole task is measured in insertion time and average/maximum force level. The fact that a better performance can be obtained in this way, demonstrates the importance of model-free learning techniques for repetitive robotic assembly tasks. The paper presents the approach and simulation results. Keywords: robotic assembly, peg-into-hole, artificial neural networks, reinforcement learning.
`.
Question: Which category should Paper #245288 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #245288 has the title `Learning the Peg-into-Hole Assembly Operation with a Connectionist Reinforcement Technique  
` and the abstract `The paper presents a learning controller that is capable of increasing insertion speed during consecutive peg-into-hole operations, without increasing the contact force level. Our aim is to find a better relationship between measured forces and the controlled velocity, without using a complicated (human generated) model. We followed a connectionist approach. Two learning phases are distinguished. First the learning controller is trained (or initialised) in a supervised way by a suboptimal task frame controller. Then a reinforcement learning phase follows. The controller consists of two networks: (1) the policy network and (2) the exploration network. On-line robotic exploration plays a crucial role in obtaining a better policy. Optionally, this architecture can be extended with a third network: the reinforcement network. The learning controller is implemented on a CAD-based contact force simulator. In contrast with most other related work, the experiments are simulated in 3D with 6 degrees of freedom. Performance of a peg-into-hole task is measured in insertion time and average/maximum force level. The fact that a better performance can be obtained in this way, demonstrates the importance of model-free learning techniques for repetitive robotic assembly tasks. The paper presents the approach and simulation results. Keywords: robotic assembly, peg-into-hole, artificial neural networks, reinforcement learning.
`.
Question: Which category should Paper #245288 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Reinforcement_Learning