This page has only limited features, please log in for full access.

Unclaimed
Ziqi Sheng
School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 02 July 2021 in Sensors
Reads 0
Downloads 0

Synthesizing plans for a deformable object to transit from initial observations to goal observations, both of which are represented by high-dimensional data (namely “raw” data), is challenging due to the difficulty of learning abstract state representations of raw data and transition models of continuous states and continuous actions. Even though there have been some approaches making remarkable progress regarding the planning problem, they often neglect actions between observations and are unable to generate action sequences from initial observations to goal observations. In this paper, we propose a novel algorithm framework, namely AGN. We first learn a state-abstractor model to abstract states from raw observations, a state-generator model to generate raw observations from states, a heuristic model to predict actions to be executed in current states, and a transition model to transform current states to next states after executing specific actions. After that, we directly generate plans for a deformable object by performing the four models. We evaluate our approach in continuous domains and show that our approach is effective with comparison to state-of-the-art algorithms.

ACS Style

Ziqi Sheng; Kebing Jin; Zhihao Ma; Hankz-Hankui Zhuo. Action Generative Networks Planning for Deformable Object with Raw Observations. Sensors 2021, 21, 4552 .

AMA Style

Ziqi Sheng, Kebing Jin, Zhihao Ma, Hankz-Hankui Zhuo. Action Generative Networks Planning for Deformable Object with Raw Observations. Sensors. 2021; 21 (13):4552.

Chicago/Turabian Style

Ziqi Sheng; Kebing Jin; Zhihao Ma; Hankz-Hankui Zhuo. 2021. "Action Generative Networks Planning for Deformable Object with Raw Observations." Sensors 21, no. 13: 4552.