Evidence map›Paper›PMID 33097742›Full record

ArticleScientific reports2020

A hybrid and scalable brain-inspired robotic platform.

Zhe Zou, Rong Zhao, Yujie Wu, Zheyu Yang, Lei Tian, Shuang Wu, Guanrui Wang, Yongchao Yu, Qi Zhao, Mingwang Chen and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. When neuro-robots go wrong: A review.Frontiers in neurorobotics · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Zhe ZouCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Rong ZhaoCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Yujie WuCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Zheyu YangCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Lei TianCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Shuang WuCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Guanrui WangCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Yongchao YuDepartment of Automation, Tsinghua University, Beijing, 100084, China.
Qi ZhaoCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Mingwang ChenCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Jing PeiCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
Feng ChenDepartment of Automation, Tsinghua University, Beijing, 100084, China.
Youhui ZhangDepartment of Computer Science and Technology, Tsinghua University, Beijing, 100084, China.
Sen SongDepartment of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Mingguo ZhaoCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China. mgzhao@tsinghua.edu.cn.
Luping ShiCenter for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China. lpshi@tsinghua.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots. Here, we report a brain-inspired robotic platform implemented by an unmanned bicycle that exhibits scalability of network scale, quantity and diversity to handle the changing needs of different scenarios. The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling flexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross-paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non-spike form. The platform achieved various real-time tasks concurrently in different real-world scenarios, providing a new pathway to enhance robots' intelligence.

Identifiers

PMID33097742
PMCPMC7584638

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.