REVIEW 5 major objections 5 minor 26 references
Adaptive Wiping: Adaptive contact-rich manipulation through few-shot imitation learning with Force-Torque feedback and pre-trained object representations
T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read With eight demonstrations and real-time force-torque feedback, a robot wipes unseen surfaces and sponges at 96% of human reference force.
desk verdict Useful closed-loop extension of a haptic few-shot imitation framework, but the 96% headline rests on reference forces for unseen sponges whose provenance is never documented. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing mechanism is the FT feedback loop $\phi_{\mathrm{ft}}-\theta_{\mathrm{height}}$: a two-layer temporal convolutional network ($\phi_{\mathrm{ft}}$) with 25 hidden channels encodes the five most recent force-torque samples into a six-dimensional latent state $Z_{\mathrm{ft}}$, which is concatenated with the five-dimensional pre-trained sponge-property code $Z_{\mathrm{sponge}}$ from the VAE encoder $\phi_{\mathrm{sponge}}$; a two-layer decoder $\theta_{\mathrm{height}}$ then outputs the next vertical displacement $\Delta\hat{h}_{t+1}$. The loop is trained on the few demonstrations by minimizing the MSE between predicted and demonstrated vertical displacements, and at deployment it runs online while the planar wiping trajectory is replayed open-loop. The frozen pre-trained encoder supplies the only object-property information the loop has for unseen sponges, so $Z_{\mathrm{sponge}}$ carries the adaptation to stiffness and friction. This split between an offline planar path and an online one-dimensional vertical correction is what lets a small demonstration set and real-time force sensing coexist.
What would settle it
Have independent human demonstrators wipe with each of the nine custom sponges on the same surfaces, measure their applied forces, and re-run the 40-scenario evaluation against those measured references; if the robot's average ratio falls well below 96% or varies strongly across sponges, the claim of adaptation to unseen sponge properties is not established by the current metric. A second check would be to ablate the sponge latent code during deployment: if force tracking is unchanged when $Z_{\mathrm{sponge}}$ is randomized or fixed, the pre-trained representation is not the mechanism driving the adaptation.
Extended reading notes
Core claim
The central claim is that coupling a pre-trained latent representation of deformable-object properties with an online force-torque feedback loop converts a few-shot imitation policy into an adaptive force controller. Given eight kinesthetic demonstrations of table wiping with one sponge, the robot replays the planar wiping path offline from the demonstrated motion while predicting the end-effector's next vertical displacement online from the past five force-torque readings and the latent code of the sponge currently in hand. This closed-loop vertical prediction is what allows the robot to keep a demonstration-comparable force when the table is low, high, sloped, or replaced by a vertical wall, and across sponges whose stiffness and friction were never seen in training. The paper reports an average of 96% of the reference force over all 40 scenarios, versus 4% for the open-loop baseline and 42% for admittance control, and 104% for the wall-wiping transfer.
Load-bearing premise
The reported 96% figure assumes the per-sponge reference forces in Table II are genuine human-demonstration benchmarks for each of the ten sponges, whereas the dataset section only documents collecting demonstrations with the normal sponge.
Editorial extensions
If this is right
- A wiping policy trained on eight demonstrations of a single sponge can maintain full contact and near-demonstration force across surface heights it never saw, including a sloped table and a vertical wall.
- For deformable, elastic tools, the method removes the need to specify a target force or target position in advance, which is the practical obstacle that makes admittance and impedance control awkward in this setting.
- The offline/online split reduces the environment-adaptation problem to one axis, so the same architecture is a candidate template for other contact-rich manipulations with one uncertain contact axis.
- The measured variability is about three times that of admittance control, meaning the near-target average force comes with a consistency cost that future versions need to reduce.
Reading between the lines
- The same latent-code-plus-feedback recipe could transfer to scrubbing, polishing, or in-hand soft-object manipulation, wherever a demonstrated path exists and the uncertain axis is the one in contact.
- Ablating $Z_{\mathrm{sponge}}$ during deployment, by randomizing or fixing it to a constant, would test whether the pre-trained representation actually carries the sponge adaptation; the paper does not report this experiment.
- The reported averages across height settings may smooth over systematic errors on extreme sponges; checking the per-sponge ordering of applied force against each sponge's stiffness/friction would reveal whether the 96% average hides a compensating pattern.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes a few-shot imitation-learning framework for contact-rich wiping that combines a pre-trained haptic representation of object properties with a real-time force-torque feedback loop. The method first pre-trains a VAE encoder on simulated exploratory FT trajectories to obtain a sponge-property latent code, then trains a motion-trajectory decoder and an FT feedback decoder on eight kinesthetic demonstrations with a single normal sponge. At deployment, the robot collects an exploratory FT trajectory for the current sponge, encodes it to obtain z_sponge, generates an offline planar wiping trajectory, and uses the FT feedback loop to predict online vertical displacements. The experiments cover 40 scenarios (10 sponges x 4 surface types, including a wall) and compare against a no-feedback imitation baseline and an admittance controller. The headline result is that the method applies, on average, 96% of a per-sponge reference force, whereas the baseline achieves 4% and admittance control 42%.
Significance. If the quantitative claims hold, the paper makes a useful empirical contribution: it shows that a small number of demonstrations, combined with a pre-trained haptic representation and online FT feedback, can adapt a wiping policy to unseen surface heights and unseen deformable-object properties on real hardware. The 40-scenario deployment study is substantial, and the ablation of the FT-loop depth, TCN window size, and number of demonstrations in the appendix is a positive feature. The comparison to a no-feedback imitation baseline is relevant and the wall-wiping test is an interesting stress test. However, the strength of the central 96% claim depends on the provenance of the per-sponge reference forces and on statistical support that the current single-trial presentation does not provide.
major comments (5)
- [IV-C2, Table II] The 96% headline figure is computed relative to the reference forces in Table II, yet the paper documents human demonstrations only for the normal sponge ('We collected 8 demonstrations using a normal sponge', Section IV-C2). No protocol is given for obtaining the reference forces listed for the nine custom sponges (e.g., s1f1 -22.8 N, s3f3 -36.7 N). If those values were not obtained from genuine human demonstrations performed with each sponge under the same 'apply as much force as possible' instruction, then the denominator of the 96% ratio is not an independent external benchmark, and the abstract's claim of applying 'reference forces' is not established. Please specify the measurement protocol, including the number of demonstrations per sponge and whether the same demonstrator and instruction were used.
- [III-B2, III-C, Table IV] The FT feedback decoder θ_height is trained only on the normal sponge: the eight demonstrations (IV-C2) and the single real unlabeled trajectory (IV-C1) all use the normal sponge, so during training z_sponge is effectively a single constant vector. At deployment the same decoder receives z_sponge codes of nine unseen sponges. The paper provides no analysis or ablation showing that θ_height output varies appropriately with z_sponge; the appendix ablates FT-loop depth, TCN window size, and number of demonstrations, but not the pretrained encoder, and instead refers to [16] for pre-training ablations. Without evidence that the decoder responds meaningfully to z_sponge, the claim that the method adapts to unseen sponge properties through the pretrained representation is unsupported.
- [Table I, V-A, V-B] Each reported condition appears to be a single trial: the standard deviations in Table I are within-trajectory across time steps, not across independent runs. No confidence intervals or statistical tests are provided. Given the large within-trajectory variability in the proposed method (e.g., s1f1 low: -18.0 ± 11.9 N; s1f2 low: -28.7 ± 18.0 N), the claims that the method 'significantly outperforms' baselines and that the applied force does not 'significantly vary' with height are not statistically supported.
- [V-C, Table III] The wall-wiping evaluation compares force measurements in a gravity-neutral configuration against the Table II reference forces, which were measured during table wiping where the vertical force includes the sponge's weight (as acknowledged by the baseline's positive no-contact force in Section V-A). If no gravity compensation or offset was applied in the wall configuration, then the ratios in Table III (e.g., Normal 115%, s1f1 104%) are not directly comparable to the reference, and the claim of '104% of the reference force' for wall wiping is not established.
- [V, Eq. (4)] The admittance control baseline is given a target force defined as 'the force applied when the sponge is pressed by 1cm' (Section V). This target is arbitrary and not derived from the task or from human demonstrations, so the AC comparison does not demonstrate that admittance control cannot adapt to sponge properties; it only shows that this particular target choice yields lower forces than the reference. The comparison to the no-feedback baseline (Aoyama et al.) is more informative.
minor comments (5)
- [Abstract] The phrase '96% accuracy in applying reference forces' is misleading: the quantity is an average ratio of applied force to reference force, not a classification accuracy. Please rephrase to 'on average applied 96% of the reference force' or similar.
- [IV-C1] The statement that 'the FT trajectories of the unlabeled data collected both in simulation and in the real world were similar' is unquantified; please provide a similarity metric or illustrative comparison.
- [IV-C2] The phrase '1 demonstration unlabeled data' is unclear; it should be 'one unlabeled trajectory' or 'one real-world unlabeled trajectory'.
- [Table I] The 'Average' row of Table I is difficult to read because it contains multiple numbers without clear column alignment; please reformat so that each column (Contact, Average [N], Std) is explicit for the average row as well.
- [V-A] The sentence 'with the standard deviation being only about 5% larger than that of human demonstrations' is ambiguous: it is not clear whether the comparison is across trials, across heights, or within trajectories. Please clarify and report the corresponding numbers.
Circularity Check
No significant circularity: the 96% figure is an empirical ratio against human-demonstration reference forces, not a quantity fitted by the proposed method.
full rationale
The derivation chain is self-contained. The pre-training objective (Eq. 1) is a standard VAE reconstruction over simulated exploratory FT trajectories and does not encode the target reference forces. The trajectory decoder (Eq. 2) and FT feedback loop (Eq. 3) are trained with MSE against demonstrated motions (xdemo and delta-hdemo), not against the reference vertical forces used later for evaluation. The headline 96% is computed as the ratio of measured average z-force to the Table II reference forces attributed to human demonstrations; no equation defines the predicted force as the reference force, and no parameter is fitted to the evaluation metric. The main caveat is a reporting gap: Section IV-C2 documents only 8 training demonstrations with the normal sponge, while Table II lists reference forces for all 9 custom sponges; this is a reproducibility and provenance question, not a circular reduction. The reliance on [16] for the pre-training design and the 'max force maximizes cleaning' premise is a citation of prior work; the novel closed-loop adaptation is validated against [16], admittance control, and wall wiping, so the central claim does not reduce to a self-citation. The conclusion's limitation statement about deformable objects is acknowledged and does not import the target result. Overall, no circular derivation step is present.
Assumptions & free parameters
free parameters (6)
- beta =
0.06
- z_sponge dimension =
5
- z_ft dimension =
6
- TCN window size =
5
- number of demonstrations =
8
- AC target force =
force when pressed 1 cm
assumptions (4)
- domain assumption Applying as much force as possible maximizes wiping/cleaning efficiency.
- domain assumption The 5-dimensional latent representation Z_sponge captures stiffness, friction, and other physical properties of a sponge.
- domain assumption Simulation and real-world FT trajectories are similar enough to allow Sim2Real transfer.
- domain assumption The reference forces in Table II are valid human-demonstration benchmarks for every sponge.
Cite this review
Pith. "Pith review of Adaptive Wiping: Adaptive contact-rich manipulation through few-shot imitation learning with Force-Torque feedback and pre-trained object representations." pith.science (2026). https://pith.science/paper/QZEAU3SV
@misc{pith2026250506451,
author = {Pith},
title = {Pith review of: Adaptive Wiping: Adaptive contact-rich manipulation through few-shot imitation learning with Force-Torque feedback and pre-trained object representations},
year = {2026},
howpublished = {\url{https://pith.science/paper/QZEAU3SV}},
note = {Machine review of arXiv:2505.06451}
}
read the original abstract
Imitation learning offers a pathway for robots to perform repetitive tasks, allowing humans to focus on more engaging and meaningful activities. However, challenges arise from the need for extensive demonstrations and the disparity between training and real-world environments. This paper focuses on contact-rich tasks like wiping with soft and deformable objects, requiring adaptive force control to handle variations in wiping surface height and the sponge's physical properties. To address these challenges, we propose a novel method that integrates real-time force-torque (FT) feedback with pre-trained object representations. This approach allows robots to dynamically adjust to previously unseen changes in surface heights and sponges' physical properties. In real-world experiments, our method achieved 96% accuracy in applying reference forces, significantly outperforming the previous method that lacked an FT feedback loop, which only achieved 4% accuracy. To evaluate the adaptability of our approach, we conducted experiments under different conditions from the training setup, involving 40 scenarios using 10 sponges with varying physical properties and 4 types of wiping surface heights, demonstrating significant improvements in the robot's adaptability by analyzing force trajectories. The video of our work is available at: https://sites.google.com/view/adaptive-wiping
Figures
Figures from the paper (5 more)
Reference graph
Works this paper leans on
-
[16]
M. Y . Aoyama and et al., “Few-shot learning of force-based motions from demonstration through pre-training of haptic representation,” in Proc. of the IEEE Int. Conf. on Robotics and Automation , 2023
work page 2023
-
[1]
Imitation learning: A survey of learning meth- ods,
A. Hussein and et al., “Imitation learning: A survey of learning meth- ods,” ACM Computing Surveys (CSUR) , vol. 50, no. 2, pp. 1–35, 2017
work page 2017
-
[2]
Y . Duan and et al., “One-shot imitation learning,” in Advances in Neural Information Processing Systems, I. Guyon, U. von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc
-
[3]
Trends and challenges in robot manipulation,
A. Billard and D. Kragic, “Trends and challenges in robot manipulation,” Science, vol. 364, no. 6446, p. eaat8414, 2019
2019
-
[4]
R. Mart ´ın-Mart´ın and et al., “Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks,” in IEEE/RSJ Int. Conf. on intelligent robots and systems , 2019, pp. 1010–1017
work page 2019
-
[5]
Learning contact-rich assembly skills using residual admittance policy,
O. Spector and M. Zacksenhouse, “Learning contact-rich assembly skills using residual admittance policy,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems , 2021, pp. 6023–6030
work page 2021
-
[6]
L. Rozo, D. Bruno, S. Calinon, and D. G. Caldwell, “Learning optimal controllers in human-robot cooperative transportation tasks with position and force constraints,” in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2015, pp. 1024–1030
work page 2015
-
[7]
K. Yamane, Y . Saigusa, S. Sakaino, and T. Tsuji, “Soft and rigid object grasping with cross-structure hand using bilateral control-based imitation learning,” IEEE Robotics and Automation Letters , 2023
work page 2023
Show all 26 references
-
[8]
Self-supervised correspondence in visuomotor policy learning,
P. Florence and et al., “Self-supervised correspondence in visuomotor policy learning,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 492–499, 2019
2019
-
[9]
The surprising effectiveness of representation learning for visual imitation,
J. Pari and et al., “The surprising effectiveness of representation learning for visual imitation,” CoRR, vol. abs/2112.01511, 2021
2021 arXiv
-
[10]
Dexterity from touch: Self-supervised pre- training of tactile representations with robotic play,
I. Guzey and et al., “Dexterity from touch: Self-supervised pre- training of tactile representations with robotic play,” arXiv preprint arXiv:2303.12076, 2023
2023 arXiv
-
[11]
Learning dynamic tactile sensing with robust vision-based training,
O. Kroemer and et al., “Learning dynamic tactile sensing with robust vision-based training,” IEEE Trans. on robotics, vol. 27, no. 3, pp. 545– 557, 2011
2011
-
[12]
Domain randomization for sim2real transfer,
L. Weng, “Domain randomization for sim2real transfer,” lilian- weng.github.io, 2019
2019
-
[13]
Domain randomization for transferring deep neural networks from simulation to the real world,
J. Tobin and et al., “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ Int. Conf. on intelligent robots and systems , 2017, pp. 23–30
2017
-
[14]
Sim-to-real transfer of robotic control with dynamics randomization,
X. B. Peng and et al., “Sim-to-real transfer of robotic control with dynamics randomization,” in 2018 IEEE Int. Conf. on robotics and automation, 2018, pp. 3803–3810
2018
-
[15]
Learning to manipulate deformable objects without demonstrations,
Y . Wu, W. Yan, T. Kurutach, L. Pinto, and P. Abbeel, “Learning to manipulate deformable objects without demonstrations,” in Robotics: Science and Systems , 2020. [Online]. Available: https://doi.org/10. 15607/RSS.2020.XVI.065
2020
-
[17]
Impedance control: An approach to manipulation: Part ii—implementation,
N. Hogan, “Impedance control: An approach to manipulation: Part ii—implementation,” 1985
1985
-
[18]
Adaptive admittance control: An approach to explicit force control in compliant motion,
H. Seraji, “Adaptive admittance control: An approach to explicit force control in compliant motion,” in Proceedings of the 1994 IEEE Int. Conf. on Robotics and Automation , 1994, pp. 2705–2712
1994
-
[19]
Auto-encoding variational bayes,
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013
2013 arXiv
-
[20]
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,
S. Bai and et al., “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling,” arXiv:1803.01271, 2018
2018 arXiv
-
[21]
Learning quadrupedal locomotion over challenging terrain,
J. Lee and et al., “Learning quadrupedal locomotion over challenging terrain,” Science Robotics, vol. 5, no. 47, p. eabc5986, 2020
2020
-
[22]
Empirical evaluation of gated recurrent neural networks on sequence modeling,
J. Chung, C. Gulcehre, K. Cho, and Y . Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” in Proc. NeurIPS Workshop Deep Learn. , 2014
2014
-
[23]
robosuite: A modular simulation framework and benchmark for robot learning,
Y . Zhu and et al., “robosuite: A modular simulation framework and benchmark for robot learning,” in arXiv preprint arXiv:2009.12293 , 2020
2009 arXiv
-
[24]
Ros: an open-source robot operating system,
M. Quigley and et al., “Ros: an open-source robot operating system,” in ICRA workshop on open source software , vol. 3, no. 3.2. Kobe, Japan, 2009, p. 5
2009
-
[25]
A tutorial survey and comparison of impedance control on robotic manipulation,
P. Song, Y . Yu, and X. Zhang, “A tutorial survey and comparison of impedance control on robotic manipulation,” Robotica, vol. 37, no. 5, p. 801–836, 2019
2019
-
[26]
Palm-e: An embodied multimodal language model,
D. Driess and et al., “Palm-e: An embodied multimodal language model,” in arXiv preprint arXiv:2303.03378 , 2023
2023 arXiv
Reviewed August 15, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.