Evidence map›Paper›PMID 36904597›Full record

ReviewSensors (Basel, Switzerland)2023

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects.

Halid Abdulrahim Kadi, Kasim Terzić

Full text readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2023. 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. Review
  2. Article
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

2 authors.

Halid Abdulrahim KadiSchool of Computer Science, University of St Andrews, Jack Cole Building, North Haugh, St Andrews KY16 9SX, UK.ORCID 0000-0001-9290-467X
Kasim TerzićSchool of Computer Science, University of St Andrews, Jack Cole Building, North Haugh, St Andrews KY16 9SX, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs' many degrees of freedom (DoF) introduce severe self-occlusion and complex state-action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms.

Indexed as

cloth-like deformable objectsdeep imitation learningdeep reinforcement learninggeneral embodied AIhuman–robot interactionknot theoryrobotics

Identifiers

PMID36904597
PMCPMC10007406

What OpenQuestion holds

Textfull text, public
LicenceCC BY
reference markers read98
measurements read18
table measurements read1
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.