Author name cluster

Erhan Öztop

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

9 papers

1 author row

ICRA Conference 2023 Conference Paper

Bimanual Rope Manipulation Skill Synthesis through Context Dependent Correction Policy Learning from Human Demonstration

T. Baturhan Akbulut
Gülsüm Tuba Çibuk Girgin
Arash Mehrabi
Minoru Asada
Emre Ugur
Erhan Öztop

Learning from demonstration (LfD) with behavior cloning is attractive for its simplicity; however, compounding errors in long and complex skills can be a hindrance. Considering a target skill as a sequence of motor primitives is helpful in this respect. Then the requirement that a motor primitive ends in a state that allows the successful execution of the subsequent primitive must be met. In this study, we focus on this problem by proposing to learn an explicit correction policy when the expected transition state between primitives is not achieved. The correction policy is learned via behavior cloning by the use of Conditional Neural Motor Primitives (CNMPs) that can generate correction trajectories in a context-dependent way. The advantage of the proposed system over learning the complete task as a single action is shown with a table-top setup in simulation, where an object has to be pushed through a corridor in two steps. Then, the applicability of the proposed method to bi-manual knotting in the real world is shown by equipping an upper-body humanoid robot with the skill of making knots over a bar in 3D space.

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IROS Conference 2016 Conference Paper

A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression

Luka Peternel
Erhan Öztop
Jan Babic

We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot. We employed the human-in-the-loop teaching paradigm to teleoperate and demonstrate a complex task execution to the robot in real-time. As the human guides the robot to perform the task, the robot obtains the skill online during the demonstration. To encode the robotic skill we employed Locally Weighted Regression that fits local models to specific state region of the task based on the human demonstration. If the robot is in the state region where no local models exist, the control over the robotic mechanism is given to the human to perform the teaching. When local models are gradually obtained in that region, the control is given to the robot so that the human can examine its performance already during the demonstration stage, and take actions accordingly. This enables a co-adaptation between the agents and contributes to a faster and more efficient teaching. As a proof-of-concept, we realised the proposed robot teaching system on a haptic robot with the task of generation of a desired vertical force on a horizontal plane with unknown stiffness properties.

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ICRA Conference 2012 Conference Paper

A kernel-based approach to direct action perception

Oliver Kroemer
Emre Ugur
Erhan Öztop
Jan Peters 0001

The direct perception of actions allows a robot to predict the afforded actions of observed objects. In this paper, we present a non-parametric approach to representing the affordance-bearing subparts of objects. This representation forms the basis of a kernel function for computing the similarity between different subparts. Using this kernel function, together with motor primitive actions, the robot can learn the required mappings to perform direct action perception. The proposed approach was successfully implemented on a real robot, which could then quickly learn to generalize grasping and pouring actions to novel objects.

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IROS Conference 2012 Conference Paper

Self-discovery of motor primitives and learning grasp affordances

Emre Ugur
Erol Sahin
Erhan Öztop

Human infants practice their initial, seemingly random arm movements for transforming them into voluntary reaching and grasping actions. With the developing perceptual abilities, infants further explore their environment using the behavior repertoire they have developed, and learn causality relations in the form of affordances, which they use for goal satisfaction and motor planning. This study proposes and implements a developmental progression on a robotic system mimicking the aforementioned infant development stages: An anthropomorphic robot hand with one basic action of swing-hand and the palmar reflex (i. e. the enclosure of the fingers upon contact) at its disposal, executes swing-hand action targeted to a salient object with different hand speeds. During the executions, it monitors the changes in its sensors, automatically forming behavior primitives such as ‘grasp’, ‘hit’, ‘carry-object’ and ‘drop’ by segmenting and differentiating the initial swing-hand action. The study then focuses on one of these behaviors, namely grasping, and shows how further practice allows the robot to learn affordances of more complex objects, which can be further used to make plans to achieve desired goals using the discovered behavior repertoire.

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ICRA Conference 2011 Conference Paper

Going beyond the perception of affordances: Learning how to actualize them through behavioral parameters

Emre Ugur
Erhan Öztop
Erol Sahin

In this paper, we propose a method that enables a robot to learn not only the existence of affordances provided by objects, but also the behavioral parameters required to actualize them, and the prediction of effects generated on the objects in an unsupervised way. In a previous study, it was shown that through self-interaction and self-observation, analogous to an infant, an anthropomorphic robot can learn object affordances in a completely unsupervised way, and use this knowledge to make plans in its perceptual space. This paper extends the affordances model proposed in that study by using parametric behaviors and including the behavior parameters into affordance learning and goal-oriented plan generation. Furthermore, for handling complex behaviors and complex objects (such as execution of precision grasp on a mug), the perceptual processing is improved by using a combination of local and global features. Finally, a hierarchical clustering algorithm is used to discover the affordances in non-homogenous feature space. In short, object affordances for object manipulation are discovered together with behavior parameters based on the monitored effects.

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ICRA Conference 2011 Conference Paper

Unsupervised learning of object affordances for planning in a mobile manipulation platform

Emre Ugur
Erol Sahin
Erhan Öztop

In this paper, we use the notion of affordances, proposed in cognitive science, as a framework to propose a developmental method that would enable a robot to ground symbolic planning mechanisms in the continuous sensory-motor experiences of a robot. We propose a method that allows a robot to learn the symbolic relations that pertain to its interactions with the world and show that they can be used in planning. Specifically, the robot interacts with the objects in its environment using a pre-coded repertoire of behaviors and records its interactions in a triple that consist of the initial percept of the object, the behavior applied and its effect, defined as the difference between the initial and the final percept. The method allows the robot to learn object affordance relations which can be used to predict the change in the percept of the object when a certain behavior is applied. These relations can then be used to develop plans using forward chaining. The method is implemented and evaluated on a mobile robot system with limited object manipulation capabilities. We have shown that the robot is able to learn the physical affordances of objects from range images and use them to build symbols and relations that can be used in making multi-step predictions about the affordances of objects and achieve complex goals.

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IROS Conference 2010 Conference Paper

Structured unsupervised kernel regression for closed-loop motion control

Jan Steffen
Erhan Öztop
Helge J. Ritter

Transferring human skills to dextrous robots in an easy, fast and robust way is one of the key challenges that still have to be tackled in order to bring robots to our every-day life. However, many problems remain unsolved. In particular, researchers are seeking new paradigms along with efficient and robust task representations that facilitate adaptation to new contexts and provide a means to appropriately react to unforeseen situations. In this paper, we present a new method for robot behaviour synthesis, where intrinsic characteristics of ‘Structured UKR manifolds’ [13] are used to derive a closed-loop controller based on motion data obtained by the ‘Robot Skill Synthesis via Human Learning’ paradigm [10]. We apply the method to the task of swapping Chinese health balls with a real 16 DOF robotic hand. Our results indicate that the marriage of ‘Structured UKR manifolds’ with the ‘Robot Skill Synthesis via Human Learning’ paradigm yields an efficient way of realising a dexterous manipulation capability on real robots.

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IROS Conference 2007 Conference Paper

Exploiting similarities for robot perception

Kai Welke
Erhan Öztop
Gordon Cheng
Rüdiger Dillmann

A cognitive robot system has to acquire and efficiently store vast knowledge about the world it operates in. To cope with every day tasks, a robot needs to learn, classify and recognize a manifold of different objects. Our work focuses on an object representation scheme that allows storing perceived objects in a compact way. This will enable the system to store extensive information about the world and will ease complex recognition tasks. The human visual system deploys several mechanisms to reduce the amount of information. Our goal is to develop an artificial system that mimics these mechanisms to create representations that can be used in cognitive tasks. In particular, in this paper we will present an approach that exploits similarities among different views of objects. The proposed representation scheme allows for reduction of storage required for the representation of objects and preserves the information about the similarity among objects. This is achieved by selecting 'important views' of objects, depending on their stability. Furthermore, by extending the same approach to multiple objects, we are able to exploit similarities between objects to find a common representation and to further reduce the storage requirements.

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ICRA Conference 2007 Conference Paper

Extensive Human Training for Robot Skill Synthesis: Validation on a Robotic Hand

Erhan Öztop
Li-Heng Lin
Mitsuo Kawato
Gordon Cheng

We propose a framework for skill synthesis for robots that exploits the human capacity to learn novel control tasks. The conceptual idea is to incorporate the target robotic platform into the experimenter's body schema so that it can be controlled effortlessly as if the robot were a part of the body. Once this stage is achieved, the dexterity on a task exhibited with the new external limb -the robot- can be used for designing controllers for the task under consideration. This article exemplifies the proposed framework by showing the derivation of an effective open-loop controller that can manipulate two balls with the fingers of a 16-DOF robotic hand.

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