Knowledge Commons of Institute of Automation,CAS
SOZIL: Self-Optimal Zero-shot Imitation Learning | |
Peng Hao1,2; Tao Lu1; Shaowei Cui1; Junhang Wei1; Yinghao Cai1; Shuo Wang1,2,3 | |
发表期刊 | IEEE Trans on Cognitive and Developmental System |
2021 | |
期号 | 1页码:1 |
摘要 | Zero-shot imitation learning has demonstrated its superiority to learn complex robotic tasks with less human participation. Recent studies show convincing performance under the condition that the robot follows the demonstration strictly by the learned inverse model. However, these methods are difficult to achieve satisfactory performance in imitation when the demonstration is suboptimal, and the learning of the learned inverse models is vulnerable to label ambiguity issues. In this paper, we propose Self-Optimal Zero-shot Imitation Learning (SOZIL) to tackle these problems. The contribution of SOZIL is twofold. First, Goal Consistency Loss (GCL) is designed to learn the multi-step goal-conditioned policy from exploration data. By directly using the goal state as supervision, GCL solves the label ambiguity problem caused by trajectory and action diversity. Second, Estimation-based Keyframe Extraction(EKE) is developed to optimize demonstrations. We formulate the keyframe extraction process as a path optimization problem under suboptimal control. By predicting the performance of the learned policy in executing transitions of any two states, EKE creates a directed graph containing all candidate paths and extracts keyframes by solving the graph’s shortest path problem. Furthermore, the proposed method is evaluated with various simulated and real-world robotic manipulating experiments such as cable harness assembly, rope manipulation, and block moving. Experimental results show that SOZIL achieves a superior success rate and manipulation efficiency than baselines |
关键词 | imitation learning learning from observation keyframe demonstration |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:001126639000056 |
七大方向——子方向分类 | 智能机器人 |
国重实验室规划方向分类 | 实体人工智能系统决策-控制 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47501 |
专题 | 多模态人工智能系统全国重点实验室_智能机器人系统研究 |
通讯作者 | Shuo Wang |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学人工智能学院 3.中国科学院脑科学与智能技术卓越创新中心 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Peng Hao,Tao Lu,Shaowei Cui,et al. SOZIL: Self-Optimal Zero-shot Imitation Learning[J]. IEEE Trans on Cognitive and Developmental System,2021(1):1. |
APA | Peng Hao,Tao Lu,Shaowei Cui,Junhang Wei,Yinghao Cai,&Shuo Wang.(2021).SOZIL: Self-Optimal Zero-shot Imitation Learning.IEEE Trans on Cognitive and Developmental System(1),1. |
MLA | Peng Hao,et al."SOZIL: Self-Optimal Zero-shot Imitation Learning".IEEE Trans on Cognitive and Developmental System .1(2021):1. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
SOZIL_Self-Optimal_Z(13840KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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