Robotics Education and Research at Scale: A Remotely Accessible Robotics Development Platform

Wiedmeyer, Wolfgang; Mende, Michael; Hartmann, Dennis L.; Bischoff, Rainer; Ledermann, Christoph; Kröger, Torsten

doi:10.1109/icra.2019.8793976

Cited by 15 publications

(8 citation statements)

References 15 publications

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“…Gladly, this approach also gives room to students already accustomed to Arduino programming and gives them the flexibility to write their own Arduino software. Especially for students without any programming or control background, a graphic user interface (GUI) program 6 has been developed. The GPLv3 frontend, shown in Fig.…”

Section: Programmingmentioning

confidence: 99%

“…Given the constant evolution of those robotic systems, many groups have identified that the costs of state-of-the-art manipulators with multiple degrees of freedom (DoFs) are prohibitive for many research groups [5], [6]. This has been addressed in part by the development of open-{software, hardware} systems with donated hardware [5] and internetaccessible robot experiment platforms [6], [7]. This way, the same robotic system can be shared by a larger number of researchers and students, helping to dissolve the costs of purchasing and maintaining those systems.…”

Section: Introductionmentioning

confidence: 99%

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UMIRobot: An Open-{Software, Hardware} Low-Cost Robotic Manipulator for Education

Marinho¹,

Lin²,

Zhao³

2023

Preprint

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Robot teleoperation has been studied for the past 70 years and is relevant in many contexts, such as in the handling of hazardous materials and telesurgery. The COVID19 pandemic has rekindled interest in this topic, but the existing robotic education kits fail short of being suitable for teleoperated robotic manipulator learning. In addition, the global restrictions of motion motivated large investments in online/hybrid education. In this work, a newly developed robotics education kit and its ecosystem are presented which is used as the backbone of an online/hybrid course in teleoperated robots. The students are assembled into teams, design, fabricate, and control a master device and gripper, and compete in a teleoperation challenge. The kit is low cost (< 100USD), which allows higher-learning institutions to provide one kit per student and they can learn in a risk-free environment. As of now, 53 such kits have been assembled and sent to course participants in eight countries. As major success stories, we show an example of gripper and master designed for the proposed course. In addition, we show a teleoperated task between Japan and Bangladesh executed by course participants. Design files, videos, and more information available at https: //mmmarinho.github.io/UMIRobot/ * The authors H. Lin and J. Zhao had equal contributions, described in Sections V-B and V-A, respectively.

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Section: Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UMIRobot: An Open-{Software, Hardware} Low-Cost Robotic Manipulator for Education

Marinho¹,

Lin²,

Zhao³

2023

Preprint

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“…The Robotarium [28] is a remote experimentation setup that focuses on swarm robotics. Also, the KUKA Robot Learning lab at KIT [29] offers the option to access a robotic lab with industrial robots over the internet. To the best of our knowledge, our benchmarking platform and the corresponding challenge provide the only remote and cloudbased scheme that focuses on robotic grasping and object rearrangement.…”

Section: Related Workmentioning

confidence: 99%

OCRTOC: A Cloud-Based Competition and Benchmark for Robotic Grasping and Manipulation

Liu¹,

Liu²,

Qin³

et al. 2021

Preprint

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In this paper, we propose a cloud-based benchmark for robotic grasping and manipulation, called the OCR-TOC benchmark. The benchmark focuses on the object rearrangement problem, specifically table organization tasks. We provide a set of identical real robot setups and facilitate remote experiments of standardized table organization scenarios in varying difficulties. In this workflow, users upload their solutions to our remote server and their code is executed on the real robot setups and scored automatically. After each execution, the OCRTOC team resets the experimental setup manually. We also provide a simulation environment that researchers can use to develop and test their solutions. With the OCR-TOC benchmark, we aim to lower the barrier of conducting reproducible research on robotic grasping and manipulation and accelerate progress in this field. Executing standardized scenarios on identical real robot setups allows us to quantify algorithm performances and achieve fair comparisons. Using this benchmark we held a competition in the 2020 International Conference on Intelligence Robots and Systems (IROS 2020). In total, 59 teams took part in this competition worldwide. We present the results and our observations of the 2020 competition, and discuss our adjustments and improvements for the upcoming OCRTOC 2021 competition. The homepage of the OCRTOC competition is www.ocrtoc.org, and the OCRTOC software package is available at https://github. com/OCRTOC/OCRTOC_software_package.

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“…We would like to thank Tamim Asfour for his valuable input and helpful advice. All real-world experiments were performed at the KUKA Robot Learning Lab at KIT [23]. Special thanks to Wolfgang Wiedmeyer for his tremendous effort in building up the lab.…”

Section: Acknowledgmentmentioning

confidence: 99%

TrueÆdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space

Kiemel

Weitemeyer

Meisner

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We present TrueAdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joint accelerations at regular intervals. The adapted trajectory is generated by linear interpolation of the predicted accelerations, leading to continuously differentiable joint velocities and positions. Bounded jerks, accelerations and velocities are guaranteed by calculating the valid acceleration range at each decision step and clipping the network's output accordingly. A deviation penalty during the training process causes the adapted trajectory to follow the original one. Smooth movements are encouraged by penalizing high accelerations and jerks. We evaluate our approach by training a simulated KUKA iiwa robot to balance a ball on a plate while moving and demonstrate that the balancing policy can be directly transferred to a real robot with little impact on performance.

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Robotics Education and Research at Scale: A Remotely Accessible Robotics Development Platform

Cited by 15 publications

References 15 publications

UMIRobot: An Open-{Software, Hardware} Low-Cost Robotic Manipulator for Education

UMIRobot: An Open-{Software, Hardware} Low-Cost Robotic Manipulator for Education

OCRTOC: A Cloud-Based Competition and Benchmark for Robotic Grasping and Manipulation

TrueÆdapt: Learning Smooth Online Trajectory Adaptation with Bounded Jerk, Acceleration and Velocity in Joint Space

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