Continuous trajectory optimization for autonomous humanoid door opening

Zucker, Matt; Jun, Youngbum; Killen, Brittany; Taegoo, Kim; Oh, Paul

doi:10.1109/tepra.2013.6556358

Cited by 24 publications

(12 citation statements)

References 12 publications

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“…DRC-Hubo+ earned a point from this task in the DARPA challenge. Compared to DRC-Trials, which required three different types of doors (Zucker et al, 2013), the DRC-Finals asked the participant robot to open only left-knob pushing door. Therefore, the designed compliance controller enabled DRC-Hubo+ to successfully finish the task.…”

Section: Tool Handling Tasksmentioning

confidence: 99%

“…This enabled team members to repeat the rapid-prototyping of algorithms (with Open-Hubo), T&E, and verification-andvalidation (V&V). True to Hubo's roots in open-source, the team's algorithmic approaches and results were documented in over a dozen publications and released in IEEE Xplore in spring 2013 (Alunni et al, 2013;Dang, Jun, Oh, & Allen, 2013;Grey et al, 2013;O'Flaherty et al, 2013;Rasmussen, Yuvraj, Vallett, Sohn, & Oh, 2014c;Zhang et al, 2013;Zheng et al, 2013;Zucker, Jun, Killen, Kim, & Oh, 2013). The results were also showcased at the DRC Workshop held in the Atlanta IEEE Humanoids Conference in October 2013.…”

Section: Introductionmentioning

confidence: 97%

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

Sohn

Jang

et al. 2017

Journal of Field Robotics

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This paper presents a technical overview of Team DRC-Hubo@UNLV's approach to the 2015 DARPA Robotics Challenge Finals (DRC-Finals). The Finals required a robotic platform that was robust and reliable in both hardware and software to complete tasks in 60 min under degraded communication. With this point of view, Team DRC-Hubo@UNLV integrated methods and algorithms previously verified, validated, and widely used in the robotics community. For the communication aspect, a common shared memory approach that the team adopted to enable efficient data communication under the DARPA controlled network is described. A new perception head design (optimized for the tasks of the Finals) and its data processing are then presented. In the motion planning and control aspect, various techniques, such as wheel-driven navigation, zero-momentpoint (ZMP) -based locomotion, and position-based manipulation and controls, are described in this paper. By introducing strategically critical elements and key lessons learned from DRC-Trials 2013 and the testbed of Charleston, we also illustrate how DRC-Hubo has evolved successfully toward the DRC-Finals. C 2017 Wiley Periodicals, Inc.

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Section: Tool Handling Tasksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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

Sohn

Jang

et al. 2017

Journal of Field Robotics

Self Cite

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“…Zucker presented a continuous trajectory optimization for opening a door with a humanoid robot HUBO+. Special emphasis was placed on generating smooth trajectories to minimize unnecessary motion of the robot [10].…”

Section: Related Workmentioning

confidence: 99%

Climbing over large obstacles with a humanoid robot via multi-contact motion planning

Kanajar

Caldwell

Kormushev

2017

2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Incremental progress in humanoid robot locomotion over the years has achieved important capabilities such as navigation over flat or uneven terrain, stepping over small obstacles and climbing stairs. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-body contact motion planning guided through physical human demonstrations. Our goal is to make the humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the whole-body motion and multi-body contacts including both the upper and lower body limbs. The proposed motion planning algorithm is applied to a challenging task of climbing over a large obstacle. We demonstrate successful execution of the climbing task in simulation using our multi-contact motion planning algorithm initialized via a transfer from real-world human demonstrations of the task and further optimized.

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“…The left and right arms are treated independently as there is no internal articulation in the shoulder. Experimentally, they have not been applied beyond kinematic chains with at most 6–9 DoFs (Kalakrishnan, Chitta, Theodorou, Pastor, & Schaal, ; Ratliff, Zucker, Bagnell, & Srinivasa, ; Zucker, Jun, Killen, Kim, & Oh, ). A quadruped robot was explored in Ratliff et al.…”

Section: Related Workmentioning

confidence: 99%

Team RoboSimian: Semi‐autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals

Karumanchi

Edelberg

Baldwin

et al. 2016

Journal of Field Robotics

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This paper discusses hardware and software improvements to the RoboSimian system leading up to and during the 2015 DARPA Robotics Challenge (DRC) Finals. Team RoboSimian achieved a 5th place finish by achieving 7 points in 47:59 min. We present an architecture that was structured to be adaptable at the lowest level and repeatable at the highest level. The low‐level adaptability was achieved by leveraging tactile measurements from force torque sensors in the wrist coupled with whole‐body motion primitives. We use the term “behaviors” to conceptualize this low‐level adaptability. Each behavior is a contact‐triggered state machine that enables execution of short‐order manipulation and mobility tasks autonomously. At a high level, we focused on a teach‐and‐repeat style of development by storing executed behaviors and navigation poses in an object/task frame for recall later. This enabled us to perform tasks with high repeatability on competition day while being robust to task differences from practice to execution.

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Continuous trajectory optimization for autonomous humanoid door opening

Cited by 24 publications

References 12 publications

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

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

Climbing over large obstacles with a humanoid robot via multi-contact motion planning

Team RoboSimian: Semi‐autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals

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