XBotCore: A Real-Time Cross-Robot Software Platform

Muratore, Luca; Laurenzi, Arturo; Hoffman, Enrico Mingo; Rocchi, Alessio; Caldwell, Darwin G.; Tsagarakis, Nikos G.

doi:10.1109/irc.2017.45

Cited by 75 publications

(47 citation statements)

References 15 publications

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“…To demonstrate that, we use the humanoid robot WALK-MAN [29] to perform different manipulation experiments. The robot is controlled in realtime using the XBotCore framework [30]. The whole-body motion planning is generated by OpenSoT library [31], while the AffordanceNet is used to provide visual information for the robot.…”

Section: E Robotic Applicationsmentioning

confidence: 99%

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

Nguyen

Reid

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

233

181

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We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple objects and their affordances from RGB images. Our AffordanceNet has two branches: an object detection branch to localize and classify the object, and an affordance detection branch to assign each pixel in the object to its most probable affordance label. The proposed framework employs three key components for effectively handling the multiclass problem in the affordance mask: a sequence of deconvolutional layers, a robust resizing strategy, and a multi-task loss function. The experimental results on the public datasets show that our AffordanceNet outperforms recent state-of-the-art methods by a fair margin, while its end-to-end architecture allows the inference at the speed of 150ms per image. This makes our AffordanceNet well suitable for real-time robotic applications. Furthermore, we demonstrate the effectiveness of AffordanceNet in different testing environments and in real robotic applications. The source code is available at https://github.com/nqanh/affordance-net.

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Section: E Robotic Applicationsmentioning

confidence: 99%

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

Nguyen

Reid

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

233

181

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“…This ensures deterministic handling of control-related tasks with predefined time. XBotCore [1] is a RT software framework that also provides API in the format of plugin-handlers for interacting with the NRT middleware. Although this work provides a hard RT control system with external framework integration for NRT middleware, it is not suited for our application because XBotCore is limited to hardware systems that utilize EtherCAT communication.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the software framework must also be capable of handling high-level planning tasks that are computationheavy from processing dense visualization data. Precise control requires for a framework with guaranteed real-time (RT) performance that can handle low-level sensor and motor controller interface [1]. Typically, such frameworks handle deadline constraints at high frequencies close to 1 kHz.…”

Section: Introductionmentioning

confidence: 99%

Motion Generation Interface of ROS to PODO Software Framework for Wheeled Humanoid Robot

Lee

Heo

Cho

et al. 2019

2019 19th International Conference on Advanced Robotics (ICAR)

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This paper discusses the development of robot motion generation interface between a real-time software architecture and a non-real-time robot operating system. In order for robots to execute intelligent manipulation or navigation, close integration of high-level perception and low-level control is required. However, many available open-source perception modules are developed in ROS, which operates on Linux OS that don't guarantee RT performance. This can lead to non-deterministic responses and stability problems that can adversely affect robot control. As a result, many robotic systems devote RTOS for low-level motion control. Similarly, the humanoid robot platform developed at KAIST, Hubo, utilizes a custom real-time software framework called PODO. Although PODO provides easy interface for motion generation, it lacks interface to high-level frameworks such as ROS. As such, we present a new motion generation interface between ROS and PODO that enables users to generate motion trajectories through standard ROS messages while leveraging a real-time motion controller. With the proposed communication interface, we demonstrate series of manipulator tasks on the actual wheeled humanoid platform, M-Hubo. The overall communication interface responsiveness was at most 27 milliseconds.

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“…Experiments are conducted using the humanoid WALK-MAN [32]. The robot is controlled using the XBotCore software architecture [33], while the OpenSoT library [34] is used to plan full-body motion. The relevant objects and their affordances are detected using AffordanceNet framework [35].…”

Section: Robotic Applicationsmentioning

confidence: 99%

Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks

Nguyen

Kanoulas

Muratore

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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We present a new method to translate videos to commands for robotic manipulation using Deep Recurrent Neural Networks (RNN). Our framework first extracts deep features from the input video frames with a deep Convolutional Neural Networks (CNN). Two RNN layers with an encoderdecoder architecture are then used to encode the visual features and sequentially generate the output words as the command. We demonstrate that the translation accuracy can be improved by allowing a smooth transaction between two RNN layers and using the state-of-the-art feature extractor. The experimental results on our new challenging dataset show that our approach outperforms recent methods by a fair margin. Furthermore, we combine the proposed translation module with the vision and planning system to let a robot perform various manipulation tasks. Finally, we demonstrate the effectiveness of our framework on a full-size humanoid robot WALK-MAN.

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XBotCore: A Real-Time Cross-Robot Software Platform

Cited by 75 publications

References 15 publications

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

Motion Generation Interface of ROS to PODO Software Framework for Wheeled Humanoid Robot

Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks

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