Humanoid motion generation system on HRP2-JSK for daily life environment

Okada, Kunihiko; Ogura, Takashi; Haneda, A.; Fujimoto, Junya; Gravot, Fabien; Inaba, Masayuki

doi:10.1109/icma.2005.1626828

Cited by 85 publications

(31 citation statements)

References 6 publications

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“…Instead, it is favorable to seed the BiSpace planner from the beginning with as many feasible grasps as possible using the precomputed grasp tables, increasing the likelihood that at least one of the grasps can be reached. Since the EXTEND operation is not affected by the number of trees being grown, incorporating multiple goals in the goal space does not affect efficiency [12].…”

Section: Applications To Manipulation and Grasp Planningmentioning

confidence: 99%

BiSpace Planning: Concurrent Multi-Space Exploration

Diankov¹,

Ratliff²,

Ferguson³

et al. 2008

Robotics: Science and Systems IV

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Abstract-We present a planning algorithm called BiSpace that produces fast plans to complex high-dimensional problems by simultaneously exploring multiple spaces. We specifically focus on finding robust solutions to manipulation and grasp planning problems by using BiSpace's special characteristics to explore the work and configuration spaces of the environment and robot. Furthermore, we present a number of techniques for constructing informed heuristics to intelligently search through these highdimensional spaces. In general, the BiSpace planner is applicable to any problem involving multiple search spaces.

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Section: Applications To Manipulation and Grasp Planningmentioning

confidence: 99%

BiSpace Planning: Concurrent Multi-Space Exploration

Diankov¹,

Ratliff²,

Ferguson³

et al. 2008

Robotics: Science and Systems IV

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“…The objective of building human representations is to extract compact, descriptive information (i.e., features) to encode and characterize a human's attributes from perception data (e.g., human shape, pose, and motion), when developing recognition or other human-centered reasoning systems. As an integral component of reasoning systems, approaches to construct human representations have been widely used in a variety of real-world applications, including video analysis [1], surveillance [2], robotics [3], human-machine interaction [4], augmented and virtual reality [5], assistive living [6], smart homes [7], education [8], and many others [9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Space-time representation of people based on 3D skeletal data: A review

Han

Reily

Hoff

et al. 2017

Computer Vision and Image Understanding

273

192

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Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Representations can be broadly categorized into two groups, depending on whether they use RGB-D information or 3D skeleton data. Recently, skeletonbased human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of benchmark datasets with skeleton data, and discuss potential future research directions.

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“…In recent years, many humanoid robots like ARMAR [1], Justin [2], HRP2 [3], Domo [4] and Twendy-One [5] have been designed to assist humans. The major tasks people expect of robots are helping in a kitchen environment, cleaning a room, and moving heavy objects [6].…”

Section: Introductionmentioning

confidence: 99%

Representation of pre-grasp strategies for object manipulation

Kappler

Chang

Przybylski

et al. 2010

2010 10th IEEE-RAS International Conference on Humanoid Robots

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Abstract-In this paper, we present a method for representing and re-targeting manipulations for object adjustment before final grasping. Such pre-grasp manipulation actions bring objects into better configurations for grasping through e.g. object rotation or object sliding. For this purpose, we propose a scaling-invariant and rotation-invariant representation of the hand poses, which is then automatically adapted to the target object to perform the selected pre-grasp manipulations. We show that pre-grasp strategies such as sliding manipulations not only enable more robust object grasping, but also significantly increase the success rate for grasping.

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Humanoid motion generation system on HRP2-JSK for daily life environment

Cited by 85 publications

References 6 publications

BiSpace Planning: Concurrent Multi-Space Exploration

BiSpace Planning: Concurrent Multi-Space Exploration

Space-time representation of people based on 3D skeletal data: A review

Representation of pre-grasp strategies for object manipulation

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