ARMAR-6: A Collaborative Humanoid Robot for Industrial Environments

Asfour, Tamim; Kaul, Lukas; Wächter, Mirko; Ottenhaus, Simon; Weiner, Pascal; Rader, Samuel; Grimm, Raphael; Zhou, You; Grotz, Markus; Paus, Fabian; Shingarey, Dmitriy; Haubert, Hans

doi:10.1109/humanoids.2018.8624966

Cited by 45 publications

(27 citation statements)

References 7 publications

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“…The purpose is to generate an appropriate robot motion to hit the ball and let the ball stop at a specific location. We conducted this experiment in the MuJoCo simulator [20] with the model of the humanoid robot ARMAR-6 [21]. 4.…”

Section: The Hit-the-ball Experiments In Mujocomentioning

confidence: 99%

Movement Primitive Learning and Generalization: Using Mixture Density Networks

Zhou

Gao

Asfour

2020

IEEE Robot. Automat. Mag.

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Section: The Hit-the-ball Experiments In Mujocomentioning

confidence: 99%

Movement Primitive Learning and Generalization: Using Mixture Density Networks

Zhou

Gao

Asfour

2020

IEEE Robot. Automat. Mag.

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“…This application has multiple sub-domains, such as logistics, assembly lines, waste management and agrifood (Fantoni et al, 2014 ). Typical tasks, such as in warehouses and production lines, require robotic systems that are cheap, robust, easy to control, and capable to reliably grasping a large variety of objects (Kragten et al, 2012 ; Asfour et al, 2018 ; SHUNK Robotics, 2019 ). For this reason in the last years many companies have exploited pneumatic actuation for the development of soft continuous grippers (Soft Robotics Company, 2019 ) or non-conventional granular grippers (Brown et al, 2010 ; Amend et al, 2016 ).…”

Section: Hands In the Fieldmentioning

confidence: 99%

Hands in the Real World

2020

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Robots face a rapidly expanding range of potential applications beyond controlled environments, from remote exploration and search-and-rescue to household assistance and agriculture. The focus of physical interaction is typically delegated to end-effectors-fixtures, grippers or hands-as these machines perform manual tasks. Yet, effective deployment of versatile robot hands in the real world is still limited to few examples, despite decades of dedicated research. In this paper we review hands that found application in the field, aiming to discuss open challenges with more articulated designs, discussing novel trends and perspectives. We hope to encourage swift development of capable robotic hands for long-term use in varied real world settings. The first part of the paper centers around progress in artificial hand design, identifying key functions for a variety of environments. The final part focuses on the overall trends in hand mechanics, sensors and control, and how performance and resiliency are qualified for real world deployment.

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“…We introduced the humanoid robot ARMAR-6 in [17]. In this article, we elaborate on the reasoning behind the design choices for developing a humanoid maintenance assistant for man-made environments and on the hardware and software codesign.…”

Section: Background and Related Workmentioning

confidence: 99%

ARMAR-6: A High-Performance Humanoid for Human-Robot Collaboration in Real-World Scenarios

Asfour

Paus

Waechter

et al. 2019

IEEE Robot. Automat. Mag.

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major goal of humanoid robotics is to enable safe and reliable human-robot collaboration in realworld scenarios. In this article, we present ARMAR-6, a new high-performance humanoid robot for various tasks, including but not limited to grasping, mobile manipulation, integrated perception, bimanual collaboration, compliant-motion execution, and natural language understanding. We describe how the requirements arising from these tasks influenced our major design decisions, resulting in vertical integration during the joint hardware and software development phases. In particular, the entire hardware-including its structure, sensor-actuator units, and low-level controllers-as well as its perception, grasping and manipulation skills, task coordination, and the entire software architecture were all developed by one team of engineers. Component interaction is facilitated by our software framework ArmarX, which

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ARMAR-6: A Collaborative Humanoid Robot for Industrial Environments

Cited by 45 publications

References 7 publications

Movement Primitive Learning and Generalization: Using Mixture Density Networks

Movement Primitive Learning and Generalization: Using Mixture Density Networks

Hands in the Real World

ARMAR-6: A High-Performance Humanoid for Human-Robot Collaboration in Real-World Scenarios

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