A Survey on Imitation Learning for Contact-Rich Tasks in Robotics

Arash Ajoudani; Francesco Nori; Gokhan Solak; Heng Zhang; Tadej Petri\v{c}; Toshiaki Tsuji; Yasuhiro Kato

REVIEW 3 major objections 5 minor 5 cited by

A Survey on Imitation Learning for Contact-Rich Tasks in Robotics

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This survey maps imitation learning for contact-rich robot tasks, arguing that force and touch, not just vision, carry the skill and that no prior survey has covered this intersection.

desk verdict A useful, readable survey of imitation learning for contact-rich tasks, but its central 'first survey' claim is contradicted by its own reference list. read the letter →

arxiv 2506.13498 v1 pith:7PFD32EW submitted 2025-06-16 cs.RO cs.HCcs.LGcs.SYeess.SY

Toshiaki Tsuji , Yasuhiro Kato , Gokhan Solak , Heng Zhang , Tadej Petrič , Francesco Nori , Arash Ajoudani This is my paper · ORCID

classification cs.ROcs.HCcs.LGcs.SYeess.SY

keywords imitationlearningcontact-richmanipulationfromdemonstrationtactilesensingdynamicmovementprimitivesbehaviorcloningfoundationmodelsrobotsurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims to be the first survey devoted specifically to imitation learning for contact-rich robotic tasks, and it organizes the field into a map of demonstration collection, learning algorithms, datasets, and applications. A sympathetic reader would care because contact-rich skills—assembly, polishing, surgery, household chores—are where robots still fail, and human demonstrations encode tacit force and compliance knowledge that is hard to specify by hand. The survey's organizing insight is that these tasks are nonlinear, sensitive to tiny positional deviations, and only partially visible through cameras, so force and tactile feedback are not optional extras but necessary complements to vision. If the map is accurate, it gives researchers and practitioners a common vocabulary for choosing teaching methods and learning algorithms, and it clarifies where the field's real bottlenecks lie.

What carries the argument

The load-bearing organizing object is the taxonomy of teaching and learning. Online versus offline teaching describes where demonstration trajectories come from—directly operating the robot, remote control, virtual reality, or sensor observation of human movement—while online versus offline learning describes when the policy updates, either during execution with feedback or from a stored dataset. Crossing these two dichotomies produces the four method families that structure the entire survey, and this frame carries the argument that imitation learning for contact-rich tasks is not a single technique but a design space. The companion mechanism is the modality argument: because contact-rich tasks are partially observable under vision and the contact point is often occluded, force and tactile sensing must be added to position and vision, and the dataset and application sections show how those modalities enter different learning algorithms.

What would settle it

A reproducible literature search with explicit inclusion and exclusion criteria for surveys covering both imitation learning and contact-rich manipulation published before June 2025 would settle the first-survey claim: if it returns a prior survey with the same scope, the central claim fails, and if it returns none, the claim stands. A second check would take the survey's own corpus and test whether its trend statements—for example, that behavior cloning and foundation models dominate recent contact-rich work—survive a quantitative tally over the full set of cited papers.

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Extended reading notes

Core claim

The survey's central claim is that no prior survey has investigated imitation learning for contact-rich tasks, and that this paper fills that gap by systematically organizing current research along two axes: how demonstrations are collected (online teaching, such as kinesthetic teaching, teleoperation, and VR-based teaching, versus offline observation of human movement) and when policies are learned (online during execution or offline from stored data). Crossing these axes yields four named categories—interactive imitation, demo-augmented reinforcement learning, direct imitation, and observational learning—which the paper uses to classify methods throughout. It further argues that contact-rich imitation learning is fundamentally multimodal: position, force, vision, and tactile signals each contribute different information, and fusion of several modalities is needed because interaction forces cannot be inferred from images alone. The survey also reviews datasets and benchmarks, showing that large general manipulation datasets exist but that touch-specific data remain smaller and mostly research-grade, and it identifies three future directions: dual-process hierarchical architectures, multimodal sensing, and improved simulation-to-reality transfer.

Load-bearing premise

The survey assumes that the papers it selected in Sections 3 through 5 are representative enough of the broader field to support its trend claims and its status as the first survey, yet it does not describe any reproducible search strategy, inclusion criteria, or quality filter.

Editorial extensions

If this is right

The four-way teaching/learning taxonomy gives a practitioner a direct way to select a method: use interactive imitation when real-time corrections are available, direct or observational learning when working from stored data, and adversarial or demo-augmented reinforcement learning when reward design is the obstacle.
The modality analysis implies that adding force or tactile sensing to a vision-only demonstration pipeline is a necessity rather than an enhancement for tasks where contact forces determine success, such as assembly or surgery, because occlusion hides the contact interface.
The dataset review anchors expectations about scale: large trajectories exist for general manipulation, but contact-specific tactile datasets are smaller and mostly research-based, meaning data availability, not algorithm choice, may be the practical ceiling for contact-rich imitation learning.
The survey's stated future directions—dual-process hierarchical architectures, multimodal sensing, and sim-to-real transfer—identify where the next advances are likely to come from if the current trend claims are correct.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

My inference: if the first-survey claim is correct, then the field's most pressing missing artifact is a shared evaluation benchmark for contact-rich imitation learning, and the datasets and applications collected here could serve as a seed for building one.
My inference: the taxonomy suggests a testable scaling relationship—methods that combine force observation during teaching with online correction during learning should outperform vision-only behavior cloning on precision tasks such as peg-in-hole with micrometer clearance.
My inference: continued progress in foundation models is likely to shift the bottleneck from policy learning to demonstration capture, making cheap force and tactile teleoperation hardware at least as important as the learning algorithms themselves.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Worth your time if you need a map of imitation learning for contact-rich manipulation, but the paper overstates its novelty. The central claim in Sec. 1 that no survey exists on IL for contact-rich tasks is false as stated. Sec. 5.2 cites An et al. (2025), a survey of imitation learning for dexterous manipulation — a domain that is contact-rich by the paper's own definition in Sec. 2.1. The authors need to either scope the claim or acknowledge An et al. and explain the difference.

What works: the 2x2 taxonomy (online/offline teaching crossed with online/offline learning) is a decent organizing frame and would help a newcomer. The DMP section is thorough and correctly distinguishes reformulation vs. parallelization. The multi-modal IL section covers force, tactile, vision, and language with reasonable depth. Most summaries are accurate.

Soft spots: the duplicated AIL/GAIL subsections (4.4.1 and 4.4.2) are nearly verbatim — one of them should go. The malformed citations are embarrassing: 'Neville 1985' should be Hogan's impedance control paper, and 'PJ and BDOA 1971' is probably Kalman's inverse optimal control work; both need fixing. No search strategy or inclusion criteria is given, so 'systematic organization' overstates what is an editorial selection. This also makes the negative claim about prior surveys harder to defend.

None of this is fatal. The survey covers the right ground and would be a solid reference if the authors fix the first-survey claim, delete the duplicate, and clean the bibliography. I would not desk-reject it; a serious referee can deal with these issues in a revision. But I would not cite it in its current form.

Referee Report

3 major / 5 minor

Summary. This paper surveys imitation learning (IL) for contact-rich robotic tasks. It organizes the area into data collection (teaching methods and sensory modalities), learning algorithms (behavior cloning, dynamic movement primitives, generative methods, inverse RL, offline RL, and other approaches), available datasets, and application domains (industrial, household/service, and healthcare robots). The paper proposes a 2x2 taxonomy that crosses online/offline teaching with online/offline learning, and it claims to be the first survey specifically devoted to IL for contact-rich tasks.

Significance. If its scope and organization are made precise, the survey would be a useful entry point for researchers entering this subfield. Its strengths include a broad collection of recent work, a sensible separation of teaching and learning concepts, a useful review of DMP variants for contact-rich manipulation, a concrete list of public datasets, and attention to force and tactile modalities that are often underrepresented in general IL surveys. Several sections, particularly the DMP and multi-modal IL discussions, are informative and well grounded in the literature. However, the central novelty claim of being the first IL survey for contact-rich tasks is not adequately delimited or supported, and the absence of a reported search strategy weakens the reproducibility of the survey's coverage.

major comments (3)

[Sec. 1] The claim that 'No survey exists that investigates imitation learning research in contact-rich tasks' is contradicted by the authors' own reference list: Sec. 5.2 cites An et al. (2025), a survey of imitation learning for dexterous manipulation (arXiv:2504.03515). Under the paper's own Sec. 2.1 definition of contact-rich manipulation as involving continuous and complex physical interactions and sophisticated force control, dexterous manipulation is a canonical contact-rich domain. The authors must either delimit 'contact-rich tasks' explicitly to exclude dexterous manipulation or explain how the present survey differs from An et al. (2025); otherwise the paper's central contribution is not the first survey but an overlapping taxonomy.
[Secs. 3-5] No search strategy, database list, inclusion or exclusion criteria, or quality filter is described anywhere in the paper. The contributions list in Sec. 1 promises a 'systematic organization' of existing research, but the reader cannot verify that the selection of papers in Secs. 3-5 is representative or comprehensive. The claim to be the first survey is a negative claim that is especially sensitive to this omission: without a defined search window and inclusion criteria, the paper cannot support either its first-survey status or its trend statements. I recommend adding a methodology subsection or explicitly characterizing the coverage as a selective overview rather than a systematic survey.
[Secs. 4.4.1 and 4.4.2] Sections 4.4.1 (Adversarial Imitation Learning) and 4.4.2 (Generative Adversarial Imitation Learning) are near-verbatim duplicates. Both describe the same GAN-based discriminator/generator framework, both cite Ho and Ermon (2016), and both claim effectiveness on pick-and-place and assembly tasks with the same citation (Li and Zou, 2023). Additionally, Sec. 3.2 classifies GAIL as an example of 'demo-augmented reinforcement learning' with online learning, while Sec. 4.4.2 treats GAIL as an IRL variant. This duplication and inconsistent classification directly weaken the survey's organizational contribution and must be corrected.

minor comments (5)

[Sec. 2.1 and reference list] The citation 'Neville, 1985' appears to be Neville Hogan's impedance control paper, but the author name is given as 'Neville H' rather than the standard 'Hogan, N.'; this should be corrected in both the text and the reference list.
[Sec. 4.4] The reference 'PJ and BDO, 1971' is not a usable bibliographic entry; it should be replaced with the actual authors and title of the inverse optimal control paper being cited.
[Secs. 4.3.1-4.3.2] The headings contain stray spacing: 'V ariational AutoEncoder' and 'F oundation models' should be 'Variational AutoEncoder' and 'Foundation models'.
[Sec. 4.6] The offline reinforcement learning section is not, by itself, imitation learning; a few sentences explaining why demo-initialized or demo-augmented offline RL methods are included in an IL survey would clarify the scope, especially since Sec. 3.2 already distinguishes demo-augmented RL from direct imitation.
[Throughout] Several in-text citations lack page numbers or venue details (e.g., 'Englert and Toussaint, 2017' has a DOI but no venue; 'PJ and BDO, 1971' is incomplete). A final pass to standardize the reference format would improve usability.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the survey makes no fitted predictions and its organizational claims do not reduce to their inputs.

full rationale

This is a survey paper, not a derivation. It contains no fitted parameters, no quantitative predictions, and no theorem whose conclusion is assumed in its premises. The claimed contribution is an organized mapping of imitation-learning methods for contact-rich tasks, and the body of the paper supports it with a broad literature selection across BC, DMPs, generative methods, IRL, multi-modal IL, and offline RL. Author self-citations (Tsuji, Petrič, Solak, Ajoudani, Kato, etc.) appear as illustrative examples of research in the area, not as load-bearing justifications for the survey's organizational framework; removing them would not collapse any argument. The closest thing to a load-bearing assertion is the negative novelty claim in Sec. 1: 'No survey exists that investigates imitation learning research in contact-rich tasks.' That claim is contestable because the paper itself cites An et al. (2025), a survey of imitation learning for dexterous manipulation, which is a contact-rich domain under the paper's own Sec. 2.1 definition; however, a false or under-scoped novelty claim is a correctness/scope issue, not a circular reduction of a derived result to its inputs. Since no equation is shown to equal another by construction and no fitted parameter is relabeled as a prediction, no circularity step can be exhibited under the stated hard rules, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No fitted parameters or invented entities appear because the paper is a survey. Its central claims rest on two domain-level judgments: that contact-rich tasks justify an imitation-learning focus, and that the presented selection of papers is representative. Both are asserted rather than demonstrated, and no reproducibility artifacts are provided.

assumptions (3)

domain assumption Contact-rich tasks are insufficiently solved by model-based control alone and justify a learning-from-demonstration focus.
Section 2.1 and 2.3 motivate imitation learning through nonlinearity, safety limits on reinforcement learning exploration, and limited data; this framing selects the survey's scope.
ad hoc to paper The selected literature is representative and comprehensive enough to support trend claims and the claimed first-survey status.
No search protocol, inclusion criteria, or quality filter is provided; the paper declares comprehensiveness in Section 1 and proceeds.
ad hoc to paper The 2x2 online/offline teaching and learning taxonomy is a valid organizing scheme that does not misrepresent the surveyed methods.
Figure 1 in Section 3.2 defines the four categories; the survey assumes this grouping is natural and complete, but it is not derived from the literature.

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Cite this review

Pith. "Pith review of A Survey on Imitation Learning for Contact-Rich Tasks in Robotics." pith.science (2026). https://pith.science/paper/7PFD32EW

@misc{pith2026250613498,
  author       = {Pith},
  title        = {Pith review of: A Survey on Imitation Learning for Contact-Rich Tasks in Robotics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7PFD32EW}},
  note         = {Machine review of arXiv:2506.13498}
}

read the original abstract

This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze imitation learning approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.

Figures

Figures reproduced from arXiv: 2506.13498 by the authors.

**Figure 1.** shows that IL can be categorized into the following four categories based on the combinations of online/offline teaching and online/offline learning: • Interactive Imitation • Demo-Augmented Reinforcement Learning • Direct Imitation • Observational Learning First, the combination of online teaching and online learning is called interactive imitation. The most prominent example is DAgger (Hoque et al., 2022, 2021), … view at source ↗

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Cited by 5 Pith papers

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

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, " * write output.state after.block = add.period write newline

ENTRY address author booktitle chapter doi edition editor eid howpublished institution isbn journal key month note number organization pages publisher school series title type url volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 'mid...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize ":" * " " *...

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