Learning Human Search Behavior from Egocentric Visual Inputs
“Looking for things” is a mundane but critical task we repeatedly carry on in our daily life. We introduce a method to develop a human character capable of searching for a randomly located target object in a detailed 3D scene using its locomotion capability and egocentric vision perception represent...
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| Vydáno v: | Computer graphics forum Ročník 40; číslo 2; s. 389 - 398 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford
Blackwell Publishing Ltd
01.05.2021
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | “Looking for things” is a mundane but critical task we repeatedly carry on in our daily life. We introduce a method to develop a human character capable of searching for a randomly located target object in a detailed 3D scene using its locomotion capability and egocentric vision perception represented as RGBD images. By depriving the privileged 3D information from the human character, it is forced to move and look around simultaneously to account for the restricted sensing capability, resulting in natural navigation and search behaviors. Our method consists of two components: 1) a search control policy based on an character model, and 2) an online replanning control module for synthesizing detailed kinematic motion based on the trajectories planned by the search policy. We demonstrate that the combined techniques enable the character to effectively find often occluded household items in indoor environments. The same search policy can be applied to different full body characters without the need of retraining. We evaluate our method quantitatively by testing it on randomly generated scenarios. Our work is a first step toward creating intelligent virtual agents with human‐like behaviors driven by onboard sensors, paving the road toward future robotic applications. |
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| AbstractList | “Looking for things” is a mundane but critical task we repeatedly carry on in our daily life. We introduce a method to develop a human character capable of searching for a randomly located target object in a detailed 3D scene using its locomotion capability and egocentric vision perception represented as RGBD images. By depriving the privileged 3D information from the human character, it is forced to move and look around simultaneously to account for the restricted sensing capability, resulting in natural navigation and search behaviors. Our method consists of two components: 1) a search control policy based on an abstract character model, and 2) an online replanning control module for synthesizing detailed kinematic motion based on the trajectories planned by the search policy. We demonstrate that the combined techniques enable the character to effectively find often occluded household items in indoor environments. The same search policy can be applied to different full body characters without the need of retraining. We evaluate our method quantitatively by testing it on randomly generated scenarios. Our work is a first step toward creating intelligent virtual agents with human‐like behaviors driven by onboard sensors, paving the road toward future robotic applications. “Looking for things” is a mundane but critical task we repeatedly carry on in our daily life. We introduce a method to develop a human character capable of searching for a randomly located target object in a detailed 3D scene using its locomotion capability and egocentric vision perception represented as RGBD images. By depriving the privileged 3D information from the human character, it is forced to move and look around simultaneously to account for the restricted sensing capability, resulting in natural navigation and search behaviors. Our method consists of two components: 1) a search control policy based on an character model, and 2) an online replanning control module for synthesizing detailed kinematic motion based on the trajectories planned by the search policy. We demonstrate that the combined techniques enable the character to effectively find often occluded household items in indoor environments. The same search policy can be applied to different full body characters without the need of retraining. We evaluate our method quantitatively by testing it on randomly generated scenarios. Our work is a first step toward creating intelligent virtual agents with human‐like behaviors driven by onboard sensors, paving the road toward future robotic applications. |
| Author | Liu, C. Karen Yu, Wenhao Sorokin, Maks Ha, Sehoon |
| Author_xml | – sequence: 1 givenname: Maks orcidid: 0000-0001-5994-0046 surname: Sorokin fullname: Sorokin, Maks email: maks@gatech.edu organization: Georgia Institute of Technology – sequence: 2 givenname: Wenhao orcidid: 0000-0001-8263-8224 surname: Yu fullname: Yu, Wenhao email: wenhaoyu@gatech.edu organization: Robotics at Google – sequence: 3 givenname: Sehoon orcidid: 0000-0002-1972-328X surname: Ha fullname: Ha, Sehoon email: sehoonha@gatech.edu organization: Robotics at Google – sequence: 4 givenname: C. Karen orcidid: 0000-0001-5926-0905 surname: Liu fullname: Liu, C. Karen email: karenliu@cs.stanford.edu organization: Stanford University |
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| Copyright | 2021 The Author(s) Computer Graphics Forum © 2021 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2021 The Eurographics Association and John Wiley & Sons Ltd. |
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| SubjectTerms | CCS Concepts Computing methodologies → Procedural animation; Motion processing Indoor environments Kinematics Locomotion Robotics Searching |
| Title | Learning Human Search Behavior from Egocentric Visual Inputs |
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