Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

Oh, Paul; Sohn, Kiwon; Jang, Gunhee; Jun, Youngbum; Cho, Baek-Kyu

doi:10.1002/rob.21686

Cited by 45 publications

(15 citation statements)

References 35 publications

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“…The DARPA Robotics Challenge (DRC) 2015 [1] required the development of mobile teleoperation systems. Several research groups, such as DRC-HUBO [3], CHIMP [4], Ro-boSimian [5], and our own entry Momaro [6] presented teleoperation systems with impressive manipulation capabilities. The focus was on completing as many manipulation tasks as possible using a team of trained operators.…”

Section: Related Workmentioning

confidence: 99%

Bimanual Telemanipulation with Force and Haptic Feedback and Predictive Limit Avoidance

Lenz¹,

Behnke²

2021

Preprint

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Robotic teleoperation is a key technology for a wide variety of applications. It allows sending robots instead of humans in remote, possibly dangerous locations while still using the human brain with its enormous knowledge and creativity, especially for solving unexpected problems. A main challenge in teleoperation consists of providing enough feedback to the human operator for situation awareness and thus create full immersion, as well as offering the operator suitable control interfaces to achieve efficient and robust task fulfillment. We present a bimanual telemanipulation system consisting of an anthropomorphic avatar robot and an operator station providing force and haptic feedback to the human operator. The avatar arms are controlled in Cartesian space with a 1:1 mapping of the operator movements. The measured forces and torques on the avatar side are haptically displayed directly to the operator. We developed a predictive avatar model for limit avoidance which runs on the operator side, ensuring low latency. Only off-the-shelf components were used to build the system. It is evaluated in lab experiments and by untrained operators in a small user study.

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Section: Related Workmentioning

confidence: 99%

Bimanual Telemanipulation with Force and Haptic Feedback and Predictive Limit Avoidance

Lenz¹,

Behnke²

2021

Preprint

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“…Except [47], [48], they lack more than one of the functionalities. Second, many of the existing architectures and the work done for DRC 2015 [10]- [12] require some level of human intervention to complete given tasks. Another concern is the underrepresented importance of demonstrations with physical robots for nontrivial tasks in real-world environments.…”

Section: Related Workmentioning

confidence: 99%

A Software Architecture for Service Robots Manipulating Objects in Human Environments

Nam

Lee

et al. 2020

IEEE Access

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“…At DRC, various types of robots, including humanoids, were tested on challenging tasks such as driving vehicles, getting out of cars, opening doors, turning valves, drilling walls, breaking through rough terrain, and climbing stairs. Various robots, such as ATLAS, developed by Boston Dynamics (Team WPI-CMU [ 17 ] and Team IHMC [ 18 ]), Valkyrie (NASA JSC [ 19 ]), DRC-HUBO + (Team KAIST [ 20 ], Team DRC-hubo@UNLV [ 21 ]), and THORMANG, developed by Robotis(Team ROBOTIS, Team SNU [ 22 ]), have been developed to successfully complete the aforementioned tasks.…”

Section: Introductionmentioning

confidence: 99%

Carved Turn Control with Gate Vision Recognition of a Humanoid Robot for Giant Slalom Skiing on Ski Slopes

Park

Kim

et al. 2022

Sensors

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The performance of humanoid robots is improving, owing in part to their participation in robot games such as the DARPA Robotics Challenge. Along with the 2018 Winter Olympics in Pyeongchang, a Skiing Robot Competition was held in which humanoid robots participated autonomously in a giant slalom alpine skiing competition. The robots were required to transit through many red or blue gates on the ski slope to reach the finish line. The course was relatively short at 100 m long and had an intermediate-level rating. A 1.23 m tall humanoid ski robot, ‘DIANA’, was developed for this skiing competition. As a humanoid robot that mimics humans, the goal was to descend the slope as fast as possible, so the robot was developed to perform a carved turn motion. The carved turn was difficult to balance compared to other turn methods. Therefore, ZMP control, which could secure the posture stability of the biped robot, was applied. Since skiing takes place outdoors, it was necessary to ensure recognition of the flags in various weather conditions. This was ensured using deep learning-based vision recognition. Thus, the performance of the humanoid robot DIANA was established using the carved turn in an experiment on an actual ski slope. The ultimate vision for humanoid robots is for them to naturally blend into human society and provide necessary services to people. Previously, there was no way for a full-sized humanoid robot to move on a snowy mountain. In this study, a humanoid robot that transcends this limitation was realized.

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Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

Cited by 45 publications

References 35 publications

Bimanual Telemanipulation with Force and Haptic Feedback and Predictive Limit Avoidance

Bimanual Telemanipulation with Force and Haptic Feedback and Predictive Limit Avoidance

A Software Architecture for Service Robots Manipulating Objects in Human Environments

Carved Turn Control with Gate Vision Recognition of a Humanoid Robot for Giant Slalom Skiing on Ski Slopes

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