Humanoid TeenSize Open Platform NimbRo-OP

Schwarz, Max; Pastrana, Julio; Allgeuer, Philipp; Schreiber, Michael; Schueller, Sebastian; Missura, Marcell; Behnke, Sven

doi:10.1007/978-3-662-44468-9_51

Cited by 36 publications

(31 citation statements)

References 10 publications

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“…To validate our approach, we performed experiments on the NimbRo-OP Humanoid TeenSize Open Platform robot [2] (see Fig. 1(a)).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Compliant Robot Behavior Using Servo Actuator Models Identified by Iterative Learning Control

Schwarz

Behnke

2014

RoboCup 2013: Robot World Cup XVII

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Abstract. System parameter identification is a necessary prerequisite for model-based control. In this paper, we propose an approach to estimate model parameters of robot servo actuators that does not require special testing equipment. We use Iterative Learning Control to determine the motor commands needed to follow a reference trajectory. To identify parameters, we fit a model for DC motors and friction in geared transmissions to this data using a least-squares method. We adapt the learning method for existing position-controlled servos with proportional controllers via a simple substitution. To achieve compliant position control, we apply the learned actuator models to our humanoid soccer robot NimbRo-OP. The experimental evaluation shows benefits of the proposed approach in terms of accuracy, energy efficiency, and even gait stability.

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“…To validate our approach, we performed experiments on the NimbRo-OP Humanoid TeenSize Open Platform robot [2] (see Fig. 1(a)).…”

Section: Resultsmentioning

confidence: 99%

“…From the resulting motor commands, we identify the model parameters. We evaluate this learning and identification process on our NimbRo-OP humanoid soccer robot [2]. Finally, we apply the model for feed-forward control during full-body walking motions.…”

Section: Introductionmentioning

confidence: 99%

Compliant Robot Behavior Using Servo Actuator Models Identified by Iterative Learning Control

Schwarz

Behnke

2014

RoboCup 2013: Robot World Cup XVII

Self Cite

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“…It is actuated by servos from the Dynamixel MX series. The mechatronic structure of the NimbRo-OP is best described in [3].…”

Section: Mechatronic Design Of Nimbro Teensize Robotsmentioning

confidence: 99%

Learning to Improve Capture Steps for Disturbance Rejection in Humanoid Soccer

Missura

Münstermann

Allgeuer

et al. 2014

RoboCup 2013: Robot World Cup XVII

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Abstract. Over the past few years, soccer-playing humanoid robots have advanced significantly. Elementary skills, such as bipedal walking, visual perception, and collision avoidance have matured enough to allow for dynamic and exciting games. When two robots are fighting for the ball, they frequently push each other and balance recovery becomes crucial. In this paper, we report on insights we gained from systematic push experiments performed on a bipedal model and outline an online learning method we used to improve its push-recovery capabilities. In addition, we describe how the localization ambiguity introduced by the uniform goal color was resolved and report on the results of the RoboCup 2013 competition.

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“…Many of the software are developed for humanoid robots that are control under Open Source paradigm, which shows numerous developers will be capable to modify these robots with powerful cognitive abilities. There are number of humanoid robot in worldwide, some recently developed robots such as NimbRo-OP [18], CASLHR [19], OpenCog [20], [21], and iCub [22], [23] having open source framework that have capabilities to interact with the environment and trying to achieve the required goals. They utilized their already defined pattern, stored memory, receive some information from the surrounding that are not enough for robot to work accurately and efficiently.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Cognitive Architecture for Humanoid Robot

Mushtaq¹,

Akram²,

Tariq³

et al. 2017

ijacsa

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Abstract-Humanoid robot is appearing as most popular research tool and emerging research field. The greatest challenge in the development of robot is cognition, advancement and the understanding in the human like cognition. Humanoid robot requires a self-learning behavior like the humans that is able to get the experience from environment. Based on experience, it can modify their actions, or having conscious intellectual capability to reduce empirical factual knowledge. In this regard, we propose a novel framework called an Innovative Cognitive Architecture for Humanoid Robot (ICAHR) that is capable to develop cognitive through social interaction and autonomous exploration. It combines the modules of active memory, decision processor, and sensor listener that has capability to perform selflearning behavior like human, to make decisions in dynamic environment, and perform more valid and intelligent actions with better precision. The proposed architecture may result in safe, robust, flexible, and reliable machines that can be substitute of human beings in different tasks. The feasibility of new proposed ICAHR design has been examined through real-world case studies.

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Humanoid TeenSize Open Platform NimbRo-OP

Cited by 36 publications

References 10 publications

Compliant Robot Behavior Using Servo Actuator Models Identified by Iterative Learning Control

Compliant Robot Behavior Using Servo Actuator Models Identified by Iterative Learning Control

Learning to Improve Capture Steps for Disturbance Rejection in Humanoid Soccer

An Innovative Cognitive Architecture for Humanoid Robot

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