Evidence map›Paper›PMID 40851085›Full record

ArticleScientific reports2025

A framework for robotic manipulation tasks based on multiple zero shot models.

Yifan Li, Peiyang Jiang, Chengpeng Chai, Xuyang Zhang, Chengguo Liu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Yifan LiFaculty of Engineering, University of Bristol, Bristol, BS8 1QU, UK.
Peiyang JiangFaculty of Engineering, University of Bristol, Bristol, BS8 1QU, UK.
Chengpeng ChaiSchool of Engineering, Westlake University, Hangzhou, 310030, China.
Xuyang ZhangDepartment of Engineering, King's College London, WC2R 2LS, London, UK.
Chengguo LiuState Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, 400044, China. 20220701050@stu.cqu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Humans tackle unknown tasks by integrating information from multiple sensory modalities. Existing robotic frameworks struggle to achieve effective multimodal manipulation, especially when sufficient training data is lacking. This study introduces "Panda Act", a novel robotic manipulation mechanism that leverages large language models (LLMs) and multimodal zero-shot models. The manipulation strategies are generated by LLMs as Python code, which dynamically orchestrates a suite of zero-shot visual and auditory models to fulfil task requirements. This enables robots to execute multimodal manipulations without requiring additional training. Extensive experiments in both simulated and real-world environments demonstrate that this approach excels in task comprehension, zero-shot execution, and adaptability, opening new avenues for enhancing robot adaptability in uncertain environments.

Indexed as

Models, TheoreticalRoboticsHumansLLMsRobotic manipulationUncertain environmentsZero-shot learning

Identifiers

PMID40851085
PMCPMC12375720

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.