An Approach for Robots to Deal with Objects

Hidayat, Sidiq Syamsul; Kim, Bong Keung; Ohba, Kohtaro

doi:10.5121/ijcsit.2012.4102

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Robot navigation, self-driven vehicles, and man-machine interaction are just a few examples of the countless applications that require solving the pose estimation problem (assessment of rotation and translation). This may be helpful for robots in understanding the environment and its objects which is a very important aspect in order for the robots to carry out their mission [1]. The Kalman filter (KF) is an optimal estimation algorithm for linear systems.…”

Section: Introductionmentioning

confidence: 99%

Robot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal One

Ragab¹,

Wong²

2015

IJCSIT

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In this paper, we study the effect of the layout of multiple cameras placed on top of an autonomous mobile robot. The idea is to study the effect of camera layout on the accuracy of estimated pose parameters. Particularly, we compare the performance of a vertical-stereo-pair put on the robot at the axis of rotation to that of a horizontal-stereo-pair. The motivation behind this comparison is that the robot rotation causes only a change of orientation to the cameras on the axis of rotation. On the other hand, off-axis cameras encounter additional translation beside the change of orientation. In this work, we show that for a stereo pair encountering sequences of large rotations, at least a reference camera should be put on the axis of rotation. Otherwise, the obtained translations have to be corrected based on the location of the rotation axis. This finding will help robot designers to develop vision systems that are capable of obtaining accurate pose for navigation control. An extensive set of simulations and real experiments have been carried out to investigate the performance of the studied camera layouts encountering different motion patterns. As the problem at hand is a real-time application, the extended Kalman filter (EKF) is used as a recursive estimator.

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Section: Introductionmentioning

confidence: 99%

Robot Pose Estimation: A Vertical Stereo Pair Versus a Horizontal One

Ragab¹,

Wong²

2015

IJCSIT

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“…Hidayat et al proposed affordance-based ontology for semantic robots, their model divided the robot's actions into two levels, object selection and manipulation. Based on these semantic attributes, that model could handle situations where objects appear or disappear suddenly [15,16]. Paletta et al presented the framework of reinforcement learning for perceptual cueing to opportunities for interaction of robotic agents, and features could be successfully selected that were relevant for prediction towards affordance-like control in interaction, and they believed that affordance perception was the basis cognition of robotics [17,18].…”

Section: Object's Manipulation Affordancementioning

confidence: 99%

“…Reinforcement learning [17,18] Object's manipulation affordance Incremental learning of primitive actions, and context generalization Bayesian network [6,7] Prediction and planning in bi-directional way Statistical relational [14] Model multi-object relationship Ontology knowledge [15,16] Handle object's sudden appear or disappear Support vector machine [9,10,21] Object's manipulation and traversability affordance Prediction and multi-step planning Probability graphical model [19,20] Object's traversability Table 1. Typical learning method under current affordance models…”

Section: Affordance Model Advantagesmentioning

confidence: 99%

Affordance Learning Based on Subtask's Optimal Strategy

Min

Luo

et al. 2015

International Journal of Advanced Robotic Systems

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Affordances define the relationships between the robot and environment, in terms of actions that the robot is able to perform. Prior work is mainly about predicting the possibility of a reactive action, and the object's affordance is invariable. However, in the domain of dynamic programming, a robot's task could often be decomposed into several subtasks, and each subtask could limit the search space. As a result, the robot only needs to replan its sub-strategy when an unexpected situation happens, and an object's affordance might change over time depending on the robot's state and current subtask. In this paper, we propose a novel affordance model linking the subtask, object, robot state and optimal action. An affordance represents the first action of the optimal strategy under the current subtask when detecting an object, and its influence is promoted from a primitive action to the subtask strategy. Furthermore, hierarchical reinforcement learning and state abstraction mechanism are introduced to learn the task graph and reduce state space. In the navigation experiment, the robot equipped with a camera could learn the objects' crucial characteristics, and gain their affordances in different subtasks.

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