Çetin Meriçli

Carnegie Mellon University, Robotics Institute, Senior Robotics Engineer

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Address: 10 40th street
National Robotics Engineering Center

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Papers by Çetin Meriçli

Proceedings of the Third international conference on Human Behavior Understanding

by J. Del Solar and Çetin Meriçli

Cerberus 2003 team report

by Levent Akin, Nikola Shakev, Hatice Kose, and Çetin Meriçli

Bogazici University, …, Jan 1, 2003

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Practical Extensions to Vision-Based Monte Carlo Localization Methods for Robot Soccer Domain

by Kemal Kaplan, Çetin Meriçli, Tekin Meriçli, Buluc Celik, and Levent Akin

Lecture Notes in Computer Science, 2006

This paper proposes a set of practical extensions to the vision-based Monte Carlo localization fo... more This paper proposes a set of practical extensions to the vision-based Monte Carlo localization for RoboCup Sony AIBO legged robot soccer domain. The main disadvantage of AIBO robots is that they have a narrow field of view so the number of landmarks seen in one frame is usually not enough for geometric calculation. MCL methods have been shown to be accurate and robust in legged robot soccer domain but there are some practical issues that should be handled in order to maintain stability/elasticity ratio in a reasonable level. In other words, the fast convergence ability is required in case of kidnapping. But on the other hand, fast converge can be vulnerable when an occasional bad sensor reading is received. In this work, we presented four practical extensions in which two of them are novel approaches and the remaining ones are different from the previous implementations.

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Cerberus’ 08 2-Legged Team Description Paper

by Ergin Ozkucur, Çetin Meriçli, and Can Kavaklıoglu

robot.cmpe.boun.edu.tr, 2008

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A reward function generation method using genetic algorithms: a robot soccer case study

Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages, 2010

Immediate rewards play a key role in a reinforcement learning (RL) scenario as they help the syst... more Immediate rewards play a key role in a reinforcement learning (RL) scenario as they help the system deal with the credit assignment problem. Therefore, reward function definition has a drastic effect on both how fast the system learns and to what policy it converges. It becomes even more important in case of multi-agent learning, where the state space usually gets even bigger. We propose a Genetic Algorithms (GA) based reward function shaping method for multi-robot learning problems and evaluate its performance in a robot soccer case study. A set of metrics calculated from the positions of the players and the ball on the field are used as the primitive building blocks of an immediate reward function, which is defined as a weighted combination of these metrics obtained using GA, yielding a significantly better soccer playing performance.

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Market-Driven Multi-Agent Collaboration in Robot Soccer Domain

by Çetin Meriçli and Hatice Kose

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Market-Driven Multi-Agent Collaboration in Robot Soccer Domain

by Çetin Meriçli and Hatice Kose

Cutting Edge Robotics, 2005

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CoBots: Collaborative robots servicing multi-floor buildings

by Stephanie Rosenthal, Susana Brandao, and Çetin Meriçli

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

In this video we briefly illustrate the progress and contributions made with our mobile, indoor, ... more In this video we briefly illustrate the progress and contributions made with our mobile, indoor, service robots CoBots (Collaborative Robots), since their creation in 2009. Many researchers, present authors included, aim for autonomous mobile robots that robustly perform service tasks for humans in our indoor environments. The efforts towards this goal have been numerous and successful, and we build upon them. However, there are clearly many research challenges remaining until we can experience intelligent mobile robots that are fully functional and capable in our human environments.

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Biped Walk Learning Through Playback and Corrective Demonstration

National Conference on Artificial Intelligence, 2010

Abstract Developing a robust, flexible, closed-loop walking algorithm for a humanoid robot is a c... more Abstract Developing a robust, flexible, closed-loop walking algorithm for a humanoid robot is a challenging task due to the complex dynamics of the general biped walk. Common analytical approaches to biped walk use simplified models of the physical reality. Such approaches are partially successful as they lead to failures of the robot walk in terms of unavoidable falls. Instead of further refining the analytical models, in this work we investigate the use of human corrective demonstrations, as we realize that a human can visually ...

CERBERUS 2003 TEAM REPORT

by Levent Akin, Hatice Kose, Çetin Meriçli, Nikola Shakev, and Andon Topalov

Cerberus is a joint effort of Boğaziçi University (Turkey) and Technical University of Sofia, Plo... more Cerberus is a joint effort of Boğaziçi University (Turkey) and Technical University of Sofia, Plovdiv branch(Bulgaria). We have divided the robot software design into six major tasks, and worked on those tasks in parallel. Many modules were developed using several approaches and later integrated into the final system. In addition to using better algorithms, improvements in the code were also made. iv

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All Bids for One and One Does for All: Market-Driven Multi-agent Collaboration in Robot Soccer Domain

by Kemal Kaplan, Çetin Meriçli, and Hatice Kose

International Symposium on Computer and Information Sciences, 2003

In this paper, a novel market-driven collaborative task allocation algorithm called “Collaboratio... more In this paper, a novel market-driven collaborative task allocation algorithm called “Collaboration by competition / cooperation” for the robot soccer domain is proposed and implemented. In robot soccer, two teams of robots compete with each other to win the match. For the benefit of the team, the robots should work collaboratively, whenever possible. The market-driven approach applies the basic properties of free market economy to a team of robots for increasing the profit of the team as much as possible. The experimental results show that the approach is robust and flexible and the developed team is more succcessful than its opponents.

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A Layered Metric Definition and Evaluation Framework for Multirobot Systems

Lecture Notes in Computer Science, 2009

For succesful accomplishment the top-level goal of a multirobot team should be decomposed into a ... more For succesful accomplishment the top-level goal of a multirobot team should be decomposed into a sequence of sub-goals and proper sequences of actions for achieving these subgoals should be selected and refined through execution. Selecting the proper actions at any given time requires the ability to evaluate the current state of the environment, which can be achieved by using metrics that give quantitative information about the environment. Defining appropriate metrics is already a challenging problem; however, it is even harder to assess the performance of individual metrics. This work proposes a layered evaluation scheme for robot soccer where the environment is represented in different time resolutions at each layer. A set of metrics defined on these layers together with a novel metric validation method for assessing the performance of the defined metrics are proposed.

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Multi-humanoid world modeling in Standard Platform robot soccer

2010 10th IEEE-RAS International Conference on Humanoid Robots, 2010

In the RoboCup Standard Platform League (SPL), the robot platform is the same humanoid NAO robot ... more In the RoboCup Standard Platform League (SPL), the robot platform is the same humanoid NAO robot for all the competing teams. The NAO humanoids are fully autonomous with two onboard directional cameras, computation, multijoint body, and wireless communication among them. One of the main opportunities of having a team of robots is to have robots share information and coordinate. We address the problem of each humanoid building a model of the world in real-time, given a combination of its own limited sensing, known models of actuation, and the communicated information from its teammates. Such multi-humanoid world modeling is challenging due to the biped motion, the limited perception, and the tight coupling between behaviors, sensing, localization, and communication. We describe the real-world opportunities, constraints and limitations imposed by the NAO humanoid robots. We contribute a modeling approach that differentiates among the motion model of different objects, in terms of their dynamics, namely the static landmarks (e.g., goal posts, lines, corners), the passive moving ball, and the controlled moving robots, both teammates and adversaries. We present experimental results with the NAO humanoid robots to illustrate the impact of our multi-humanoid world modeling approach. The challenges and approaches we present are relevant to the general problem of assessing and sharing information among multiple humanoid robots acting in a world with multiple types of objects.

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Guest Editorial Behavior Understanding and Developmental Robotics

by Çetin Meriçli and Javier Ruíz del Solar

IEEE Transactions on Autonomous Mental Development, 2014

The scientific, technological and application challenges that arise from the mutual interaction o... more The scientific, technological and application challenges that arise from the mutual interaction of developmental robotics and computational human behavior understanding give rise to two different perspectives. Robots need to be capable to learn dynamically and incrementally how to interpret, and thus understand multimodal human behavior, which means behavior analysis can be performed for developmental robotics. On the other hand, behavior analysis can also be performed through developmental robotics, since developmental social robots can offer stimulating opportunities for improving scientific understanding of human behavior, and especially to allow a deeper analysis of the semantics and structure of human behavior. The contributions to the Special Issue explore these two perspectives.

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Improving Biped Walk Stability Using Real-Time Corrective Human Feedback

Lecture Notes in Computer Science, 2011

Robust walking is one of the key requirements for soccer playing humanoid robots. Developing such... more Robust walking is one of the key requirements for soccer playing humanoid robots. Developing such a biped walk algorithm is non-trivial due to the complex dynamics of the walk process. In this paper, we first present a method for learning a corrective closed-loop policy to improve the walk stability for the Aldebaran Nao robot using real-time human feedback combined with an openloop walk cycle. The open-loop walk cycle is obtained from the recorded joint commands while the robot is walking using an existing walk algorithm as a blackbox unit. We capture the corrective feedback signals delivered by a human using a wireless feedback mechanism in the form of corrections to the particular joints and we present experimental results showing that a policy learned from a walk algorithm can be used to improve the stability of another walk algorithm. We then follow up with improving the open-loop walk cycle using advice operators before performing real-time human demonstration. During the demonstration, we then capture the sensory readings and the corrections in the form of displacements of the foot positions while the robot is executing improved open-loop walk cycle. We then translate the feet displacement values into individual correction signals for the leg joints using a simplified inverse kinematics calculation. We use a locally weighted linear regression method to learn a mapping from the recorded sensor values to the correction values. Finally, we use a simple anomaly detection method by modeling the changes in the sensory readings throughout the walk cycle during a stable walk as normal distributions and executing the correction policy only if a sensory reading goes beyond the modeled values. Experimental results demonstrate an improvement in the walk stability.

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World Modeling in Disaster Environments with Constructive Self-Organizing Maps for Autonomous Search and Rescue Robots

Lecture Notes in Computer Science, 2005

This paper proposes a novel approach for a Constructive Self-Organizing Map (SOM) based world mod... more This paper proposes a novel approach for a Constructive Self-Organizing Map (SOM) based world modeling for search and rescue operations in disaster environments. In our approach, nodes of the self organizing network consist of victim and waypoint classes where victim denotes a human being waiting to be rescued and waypoint denotes a free space that can be reached from the entrance of debris. The proposed approach performed better than traditional selforganizing maps in terms of both the accuracy of the output and the learning speed. In this paper the detailed explanation of the approach and some experimental results are given.

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Multi-resolution Corrective Demonstration for Efficient Task Execution and Refinement

International Journal of Social Robotics, 2012

Computationally efficient task execution is very important for autonomous mobile robots endowed w... more Computationally efficient task execution is very important for autonomous mobile robots endowed with limited on-board computational resources. Most robot control approaches assume a fixed state and action representation, and use a single algorithm to map states to actions. However, not all situations in a given task require equally complex algorithms and equally detailed state and action representations. The main motivation for this work is a desire to reduce the computational footprint of performing a task by allowing the robot to run simpler algorithms whenever possible, and resort to a more complex algorithm only when needed. We contribute the Multi-Resolution Task Execution (MRTE) algorithm that utilizes human feedback to learn a mapping from a given state to an appropriate detail resolution consisting of a state and action representation, and an algorithm providing a mapping from states to actions at that resolution. The robot learns a policy from human demonstration to switch between different detail resolutions as needed while favoring lower detail resolutions to reduce computational cost of task execution. We then present the Model Plus Correction (M+C) algorithm to improve the performance of an algorithm using corrective human feedback without modifying the algorithm itself. Finally, we introduce the Multi-Resolution Model Plus Correction (MRM+C) algorithm as a combination of MRTE and M+C. MRM+C learns how to select an appropriate detail resolution to operate at in a given state from human demonstration. Furthermore, it allows the teacher to provide corrective demonstration at different detail resolutions to improve overall task execution performance. We provide formal definitions of MRTE, M+C, and MRM+C algorithms, and show how they relate to general robot control problem and Learning from Demonstration (LfD) approach. We present experimental results demonstrating the effectiveness of proposed methods on a goaldirected humanoid obstacle avoidance task.

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Better, faster, and cheaper: what is better software?

Background: Defects are related to failures and they do not have much power for indicating a high... more Background: Defects are related to failures and they do not have much power for indicating a higher quality or a better system above the baseline that the end-users expect. Nevertheless, defect counts are commonly used as measures to capture the quality of a system. Further, the statistical association between internal design metrics and quality in terms of defects has been shown in previous work.

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Cerberus'08 Team Report

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A Robust Statistical Collision Detection Framework for Quadruped Robots

RoboCup International Symposium, 2008

In order to achieve its tasks in an effective manner, an autonomous mobile robot must be able to ... more In order to achieve its tasks in an effective manner, an autonomous mobile robot must be able to detect and quickly recover from collisions. This paper proposes a new solution to the problem of detecting collisions during omnidirectional motion of a quadruped robot equipped with an internal accelerometer. We consider this as an instance of general signal processing and statistical anomaly detection problems. We find that temporal accelerometer readings examined in the frequency domain are good indicators of regularities (normal motion) and novel situations (collisions). In the course of time, the robot builds a probabilistic model that captures its proprioceptive properties while walking without obstruction and uses that model to determine whether there is an abnormality in the case of an unfamiliar pattern. The approach does not depend on walk characteristics and the walking algorithm used, and is insensitive to the surface texture that the robot walks on as long as the surface is flat. The experiments demonstrate quite fast and successful detection of collisions independent of the point of contact with an acceptably low false positive rate.

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Çetin Meriçli

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