Pith. sign in

REVIEW 3 major objections 5 minor 40 references

Scaling Fabric-Based Piezoresistive Sensor Arrays for Whole-Body Tactile Sensing

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A daisy-chained SPI bus can stream 8,000-taxel tactile skin above 50 FPS.

desk verdict Genuinely useful SPI daisy-chain architecture for whole-body tactile sensing, but the <3.3% crosstalk headline is overstated relative to the paper's own caveat about dense multi-contact. read the letter →

arxiv 2508.20959 v1 pith:GGKY4323 submitted 2025-08-28 cs.RO eess.SP

Curtis C. Johnson , Daniel Webb , David Hill , Marc D. Killpack This is my paper
classification cs.ROeess.SP
keywords tactilesensingpiezoresistivearrayswhole-bodymanipulationSPIbuscrosstalkmitigationsoftroboticsreal-timecontrolfabricsensors
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 aims to show that whole-body tactile sensing—thousands of pressure points spread over a robot torso and arms—can be built from cheap fabric piezoresistive material without drowning in wires or bandwidth. It claims the bottleneck is not the sensor but the communication and readout topology, and proposes a daisy-chained SPI bus with custom per-sensor boards that keep all taxels synchronized. The measured system runs 1,024 taxels per board, scales to eight boards (8,192 taxels), and still clears 50 FPS at 14 MHz SPI, with crosstalk held below 3.3% by hardware grounding rather than software correction. In a whole-body grasp, tactile feedback changes an open-loop trajectory that slowly crushes a cardboard box into a gentle, stable grasp. The importance, if correct, is that a low-cost, open-source fabric skin becomes a practical real-time control input for whole-body manipulation.

What carries the argument

The load-bearing mechanism is the daisy-chained shared SPI bus together with the zero-potential (virtual-ground) readout. The bus makes all peripheral boards advance through taxel coordinates in lockstep from a single counter line, so every board reports the same taxel index at the same time; the readout's input-row guarding plus output-column virtual grounding is what keeps crosstalk small enough that raw ADC sums can be used directly as a feedback signal.

What would settle it

Apply a calibrated series of known forces to a patch of taxels and record the summed ADC output; if the sum saturates, drifts, or decreases while applied force increases, the proportional feedback law (Eq. 3) is not reliably reducing pressure. A second check: run the crosstalk test with a different multi-taxel pattern, such as a row of three adjacent taxels, and see whether ghost-taxel output stays near 3.3%.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that an SPI bus daisy chain—one shared serial bus linking up to eight readout boards—can deliver synchronized taxel streams at control rates that wireless and I2C topologies cannot reliably offer and USB hubs cannot physically scale to. Each board scans a 16x64 grid by energizing one row and reading one column at a time, while analog switches ground all inactive rows and a transimpedance amplifier holds the selected column at virtual ground, cutting parasitic current paths to under 3.3% of full scale even with three neighbor taxels saturated. The timing model t = Nout*Nin*(N*(tSPI+tproc)+tdelay) predicts linear scaling with board count, and

Load-bearing premise

The closed-loop grasping result depends on the sum of raw ADC counts over a sensor being a monotonic proxy for damaging contact force; the paper provides no force calibration and lists it as future work.

Editorial extensions

If this is right

  • At eight boards the system reaches 53 FPS, above the 50 FPS target, so an 8,192-taxel skin can serve as a real-time control input without FPGA hardware.
  • Because frame rate scales linearly with board count (Eq. 2), users can predictably trade update rate for sensor area.
  • The whole-body grasp demonstration implies the same hardware can support collision detection and pressure-limiting behaviors on soft continuum arms.
  • Host-side I/O, not the sensing or the bus, is the measured bottleneck, so moving the Python pipeline to a compiled language should reduce the 27.3 ms end-to-end latency and 1.13 ms jitter.

Reading between the lines

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

  • If the taxel-sum force proxy holds after calibration, this architecture turns a soft robot's entire surface into a low-bandwidth force envelope sensor, enabling whole-body impedance control without per-taxel force models.
  • The single three-taxel crosstalk test bounds one worst case; other contact patterns, such as a row of adjacent taxels or a large contact patch, could create different parasitic paths and should be checked before trusting the 3.3% figure globally.
  • Because each board is a 16x64 array and boards are modular, the same bus could cover arbitrary body geometry by tessellation, provided the controller has enough chip-select lines.
  • The 50 FPS target is justified by human tactile frequency perception; a natural test is whether closed-loop performance degrades measurably below that rate.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents a complete hardware and software architecture for scaling fabric-based piezoresistive tactile sensor arrays to whole-body robotic sensing. The central claims are: (i) a daisy-chained SPI bus with custom readout PCBs can stream synchronized data from up to 8,192 taxels over 1 square meter at update rates above 50 FPS; (ii) hardware crosstalk mitigation reduces 'ghost' taxel artifacts to below 3.3%; and (iii) the system enables closed-loop whole-body grasping, demonstrated by an ablation in which tactile feedback prevents crushing a deformable box. The timing model is derived analytically in Eq. (2) from algorithm structure and component timings, and empirically validated in Fig. 6. Latency and jitter are measured in dynamic impact tests, and a whole-body grasping experiment compares open-loop and closed-loop behavior. The paper is clearly written, includes open-source release of the designs, and identifies limitations such as the lack of force calibration as future work.

Significance. If the claims hold, the paper makes a useful practical contribution: it addresses a real scalability bottleneck in whole-body tactile sensing, and the open-source hardware and timing model could be reused by other groups. The frame-rate scaling result is a genuine, parameter-free derivation validated by measurements, and the CPU-load and latency analyses are pragmatic engineering characterizations. The main risk is the crosstalk claim: the '<3.3%' bound is presented as a general property but is only demonstrated for a single three-taxel configuration, while the paper itself states that residual crosstalk grows when many taxels are pressed simultaneously. The whole-body grasping demonstration is compelling but relies on uncalibrated raw ADC sums as a feedback signal. These issues affect the two headline claims, so they require additional evidence before the paper can be accepted as published.

major comments (3)
  1. [Section III-B1 / Abstract; see also Section II-B3] The claim that hardware mitigation reduces signal crosstalk to 'less than 3.3%' is supported by exactly one measurement (Fig. 9): three saturated taxels and one ghost taxel. However, Section II-B3 explicitly states that residual crosstalk 'can become more pronounced' as total current increases when many taxels are pressed simultaneously, because non-zero switch and multiplexer on-resistances create load-dependent ground-potential errors. The whole-body grasping experiment (Fig. 11) operates in the dense multi-contact regime the authors identify as worst-case. The abstract and conclusion state the 3.3% bound without this caveat. Please either characterize crosstalk as a function of the number of active taxels and contact pattern, or explicitly restrict the claim to the tested configuration.
  2. [Section III-C, Eq. (3)] The closed-loop grasping result uses the sum of raw ADC values over a sensor as the feedback signal in the proportional law of Eq. (3). No calibration or characterization is provided to show that this sum is a monotonic, non-saturating surrogate for damaging contact force; the authors list force calibration as future work (Section IV). Without such evidence, the open-loop versus closed-loop difference could be influenced by sensor nonlinearity, saturation, or the hand-tuned gain k_p rather than by genuine pressure feedback. To substantiate the claim that tactile feedback prevents crushing, the authors should provide a quantitative outcome measure (e.g., measured object deformation, contact force, or a repeatability statistic) and characterize the feedback signal's relationship to force.
  3. [Section II-D1c vs Section III-Ac] There is a factual inconsistency in the latency experiment: Section II-D1c states the impact test was repeated 50 times, while Section III-Ac reports statistics 'over 100 trials.' The measured jitter is reported as 4.64 ms, but the conclusion states 'minimal jitter (1.13 ms),' which is an estimate obtained by subtracting an assumed quantization variance. This estimate depends on the stated ±4.5 ms quantization uncertainty; the conclusion should clearly distinguish the measured jitter from the dequantized estimate and should reconcile the trial count.
minor comments (5)
  1. [General] Typos and wording: 'synronized' in Section II-C, 'quantity' should be 'quantify' in Section II-D1c, 'evidences by' should be 'evidenced by' in Section III-C, and 'compiled' should be 'compiled' in Section IV.
  2. [Table I] The qualitative ratings in Table I are said to be 'based on empirical measurements and reported behavior in literature,' but no data or reference is given for the ratings. Consider adding a short explanation or a supplementary table with the underlying measurements.
  3. [Section III-B2 / Fig. 10] The adjustable-gain demo is presented without error bars or repeated trials; since the y-axis is a normalized sum over 1024 taxels, it would be helpful to state whether the displayed curve is a single run and to quantify noise.
  4. [Section III-C / Fig. 11] The whole-body grasping results are shown as time-averaged normalized activations for a single grasp. Adding per-trial variability or at least specifying the number of repeated grasps would strengthen the ablation.
  5. [Section II-A] The sensor design is credited to prior work, but the reader would benefit from a statement of the taxel size and active area for the sensors used in the experiments, since the '1 m²' claim in the abstract is not obvious from the text.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: headline throughput/crosstalk claims are measured or derived from a deterministic timing model; the only self-citations are non-load-bearing references to the authors' prior sensor design.

full rationale

The central performance claims are empirical measurements, not circular derivations. Equation (2) is an explicit timing identity for the scan loop in Algorithm 1: ttotal = Nout * Nin * (N * (tSPI + tproc) + tdelay), with tSPI set by the 14 MHz SPI clock and tdelay by the analog filter settling time; Figure 6 then compares this model to wall-clock measurements across N=1..8 and multiple SPI clock rates. No fitted parameter is relabeled as a prediction. The 3.3% crosstalk figure (129.8 / 3886.7) is a measured ghost-taxel reading, not a derived bound; the authors themselves caution in Section II-B3 that residual crosstalk 'can become more pronounced' when 'many taxels are pressed simultaneously,' and Section IV identifies taxel-to-force calibration as future work. These are generalization/validity limitations, not circularity. The only self-citations (Refs. [29], [36]) are for the fabric sensor construction, which the paper explicitly says is 'not a novel contribution'; they are not used as a uniqueness theorem or to justify the daisy-chain SPI architecture. The closed-loop grasping ablation uses an uncalibrated sum-of-ADC feedback law (Eq. 3), but the outcome is an empirical demonstration, not a quantity defined by the feedback law. Overall, no load-bearing claim reduces by construction to its input.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The architecture rests on standard electronics assumptions (impedance, settling, virtual ground) and the domain assumption that raw ADC activation tracks contact force. The only free parameter that materially affects the headline grasp result is the hand-tuned gain kp. No new physical entities, particles, or forces are introduced.

free parameters (2)
  • kp (proportional feedback gain) = not specified
    In Eq. (3), kp multiplies the summed ADC activation to reduce joint pressure during the closed-loop grasp. The paper gives no tuning procedure or sensitivity analysis, so the successful grasping result partly depends on this hand-chosen gain.
  • Hampel filter parameters = not specified (window size, threshold typically 2-3 sigma)
    Used to remove spurious SPI/EMI spikes on the host side. The choice affects the processed signals, though it is unlikely to change the central hardware frame-rate claim.
assumptions (5)
  • domain assumption Zero-potential (virtual ground) crosstalk suppression works as analyzed in prior literature, with residual crosstalk bounded by switch on-resistance and load-dependent drops.
    Invoked in Section II-B3 and II-D2a; the paper relies on this established circuit technique rather than re-deriving it.
  • domain assumption The piezoresistive fabric taxel resistance decreases monotonically with applied pressure over the operating range.
    Used in Section II-A and in the grasp feedback law; no force calibration is provided to confirm monotonicity in situ.
  • domain assumption A 50 FPS update rate is sufficient for closed-loop whole-body tactile control because human tactile perception saturates near 50 Hz.
    Stated in Section I, supported by one neurophysiology citation; it sets the system target but is not itself validated by the paper.
  • domain assumption Cat5 cable 120-ohm characteristic impedance and adjustable termination resistors preserve signal integrity on the shared SPI/CNT lines over roughly 1.3 m cable runs.
    Described in Section II-C2; the paper does not report eye diagrams or bit-error-rate measurements.
  • domain assumption The Arduino Due's software loop and USB stack introduce deterministic per-peripheral processing overhead t_proc as modeled in Eq. (2).
    Used in the timing model and validated indirectly by frame-rate agreement, but no cycle-level verification is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Scaling Fabric-Based Piezoresistive Sensor Arrays for Whole-Body Tactile Sensing." pith.science (2026). https://pith.science/paper/GGKY4323

@misc{pith2026250820959,
  author       = {Pith},
  title        = {Pith review of: Scaling Fabric-Based Piezoresistive Sensor Arrays for Whole-Body Tactile Sensing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GGKY4323}},
  note         = {Machine review of arXiv:2508.20959}
}
read the original abstract

Scaling tactile sensing for robust whole-body manipulation is a significant challenge, often limited by wiring complexity, data throughput, and system reliability. This paper presents a complete architecture designed to overcome these barriers. Our approach pairs open-source, fabric-based sensors with custom readout electronics that reduce signal crosstalk to less than 3.3% through hardware-based mitigation. Critically, we introduce a novel, daisy-chained SPI bus topology that avoids the practical limitations of common wireless protocols and the prohibitive wiring complexity of USB hub-based systems. This architecture streams synchronized data from over 8,000 taxels across 1 square meter of sensing area at update rates exceeding 50 FPS, confirming its suitability for real-time control. We validate the system's efficacy in a whole-body grasping task where, without feedback, the robot's open-loop trajectory results in an uncontrolled application of force that slowly crushes a deformable cardboard box. With real-time tactile feedback, the robot transforms this motion into a gentle, stable grasp, successfully manipulating the object without causing structural damage. This work provides a robust and well-characterized platform to enable future research in advanced whole-body control and physical human-robot interaction.

Figures

Figures reproduced from arXiv: 2508.20959 by the authors.

Figure 1
Figure 1. Two people pressing on various parts of the robot’s sensorized surfaces [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The layers of the fabric tactile sensor, shown unfolded. From outside [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Block diagram of the sensor readout peripheral, configured to [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (7 more)
Figure 5
Figure 5. Figure 5: SPI Controller Shield (Left) and sensor readout peripheral pcb (right). [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 4
Figure 4. Figure 4: Tactile sensor PCB block diagram. 2) Hardware Implementation: The system hardware is comprised of a central controller that orchestrates communica￾tion with a series of daisy-chained peripheral boards, as shown in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Frame rates as a function number of sensors on the shared SPI bus [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: CPU utilization of both threads as a function of number of sensors. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 9
Figure 9. Figure 9: Crosstalk with associated heatmap. Phantom force appears on taxel [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Adjustable sensitivity with a fixed 500g cylindrical weight sitting on [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 12
Figure 12. Figure 12: Whole-body grasping of large empty box. Grasping without tactile [PITH_FULL_IMAGE:figures/full_fig_p009_12.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages

  1. [1]

    Scalable deep reinforcement learning for vision-based robotic manipulation,

    D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V . Vanhoucke, and S. Levine, “Scalable deep reinforcement learning for vision-based robotic manipulation,” in Proceedings of The 2nd Conference on Robot Learning. PMLR, Oct. 2018, p. 651673. [Online]. Available: https://proceedings.mlr.press/v87/kala...

    work page 2018

  2. [2]

    A comprehen- sive study of 3-d vision-based robot manipulation,

    Y . Cong, R. Chen, B. Ma, H. Liu, D. Hou, and C. Yang, “A comprehen- sive study of 3-d vision-based robot manipulation,” IEEE Transactions on Cybernetics, vol. 53, no. 3, p. 16821698, Mar. 2023

    work page 2023

  3. [3]

    The benefits of haptic feed- back in robot assisted surgery and their moderators: a meta-analysis,

    M. Bergholz, M. Ferle, and B. M. Weber, “The benefits of haptic feed- back in robot assisted surgery and their moderators: a meta-analysis,” Scientific Reports, vol. 13, p. 19215, Nov. 2023

    work page 2023

  4. [4]

    Tactile sensingfrom humans to humanoids,

    R. S. Dahiya, G. Metta, M. Valle, and G. Sandini, “Tactile sensingfrom humans to humanoids,” IEEE Transactions on Robotics , vol. 26, no. 1, p. 120, Feb. 2010

    work page 2010

  5. [5]

    Visuotactile sensors with emphasis on gelsight sensor: A review,

    A. C. Abad and A. Ranasinghe, “Visuotactile sensors with emphasis on gelsight sensor: A review,” IEEE Sensors Journal , vol. 20, no. 14, p. 76287638, Jul. 2020

    work page 2020

  6. [6]

    Hardware technology of vision-based tactile sensor: A review,

    S. Zhang, Z. Chen, Y . Gao, W. Wan, J. Shan, H. Xue, F. Sun, Y . Yang, and B. Fang, “Hardware technology of vision-based tactile sensor: A review,” IEEE Sensors Journal , vol. 22, no. 22, p. 2141021427, Nov. 2022

    work page 2022

  7. [7]

    Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation,

    A. Alspach, K. Hashimoto, N. Kuppuswamy, and R. Tedrake, “Soft-bubble: A highly compliant dense geometry tactile sensor for robot manipulation,” in 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft) , Apr. 2019, p. 597604. [Online]. Available: https://ieeexplore.ieee.org/document/8722713/?arnumber=8722713

    work page arXiv 2019

  8. [8]

    Soft-bubble grippers for robust and perceptive ma- nipulation,

    N. Kuppuswamy, A. Alspach, A. Uttamchandani, S. Creasey, T. Ikeda, and R. Tedrake, “Soft-bubble grippers for robust and perceptive ma- nipulation,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Oct. 2020, p. 99179924, 29 citations (Semantic Scholar/DOI) [2022-10-05]

    work page 2020

Show all 40 references
  1. [9]

    Stereotac: A novel visuotac- tile sensor that combines tactile sensing with 3d vision,

    E. Roberge, G. Fornes, and J.-P. Roberge, “Stereotac: A novel visuotac- tile sensor that combines tactile sensing with 3d vision,” IEEE Robotics and Automation Letters , vol. 8, no. 10, p. 62916298, Oct. 2023

    2023

  2. [10]

    A flexible and robust large scale capacitive tactile system for robots,

    P. Maiolino, M. Maggiali, G. Cannata, G. Metta, and L. Natale, “A flexible and robust large scale capacitive tactile system for robots,” IEEE Sensors Journal, vol. 13, no. 10, p. 39103917, Oct. 2013

    2013

  3. [11]

    Touch, press and stroke: a soft capacitive sensor skin,

    M. S. Sarwar, R. Ishizaki, K. Morton, C. Preston, T. Nguyen, X. Fan, B. Dupont, L. Hogarth, T. Yoshiike, R. Qiu, Y . Wu, S. Mirabbasi, and J. D. W. Madden, “Touch, press and stroke: a soft capacitive sensor skin,” Scientific Reports, vol. 13, no. 1, p. 17390, Oct. 2023

    2023

  4. [12]

    Review of recent inkjet-printed capacitive tactile sensors,

    A. Salim and S. Lim, “Review of recent inkjet-printed capacitive tactile sensors,” Sensors, vol. 17, no. 11, p. 2593, Nov. 2017

    2017

  5. [13]

    Large-area magnetic skin for multi-point and multi- scale tactile sensing with super-resolution,

    H. Hu, C. Zhang, X. Lai, H. Dai, C. Pan, H. Sun, D. Tang, Z. Hu, J. Fu, T. Li, and P. Zhao, “Large-area magnetic skin for multi-point and multi- scale tactile sensing with super-resolution,” npj Flexible Electronics , vol. 8, no. 1, p. 42, Jul. 2024

    2024

  6. [14]

    Haptitemp: A next-generation thermosensitive gelsight-like visuotactile sensor,

    A. C. Abad, D. Reid, and A. Ranasinghe, “Haptitemp: A next-generation thermosensitive gelsight-like visuotactile sensor,” IEEE Sensors Journal, vol. 22, no. 3, p. 27222734, Feb. 2022

    2022

  7. [15]

    Multimodal tactile sensing fused with vision for dexterous robotic housekeeping,

    Q. Mao, Z. Liao, J. Yuan, and R. Zhu, “Multimodal tactile sensing fused with vision for dexterous robotic housekeeping,” Nature Commu- nications, vol. 15, no. 1, p. 6871, Aug. 2024

    2024

  8. [16]

    Ele- phants evolved strategies reducing the biomechanical complexity of their trunk,

    P. Dagenais, S. Hensman, V . Haechler, and M. C. Milinkovitch, “Ele- phants evolved strategies reducing the biomechanical complexity of their trunk,” Current Biology, vol. 31, no. 21, pp. 4727–4737.e4, Nov. 2021

    2021

  9. [17]

    Tactile-sensing technologies: Trends, challenges and outlook in agri- food manipulation,

    W. Mandil, V . Rajendran, K. Nazari, and A. Ghalamzan-Esfahani, “Tactile-sensing technologies: Trends, challenges and outlook in agri- food manipulation,” Sensors, vol. 23, no. 17, 2023. [Online]. Available: https://www.mdpi.com/1424-8220/23/17/7362

    2023

  10. [18]

    Tactile sensors and other distributed sensors with minimal wiring complexity,

    M. Nilsson, “Tactile sensors and other distributed sensors with minimal wiring complexity,” IEEE/ASME Transactions on Mechatronics , vol. 5, no. 3, pp. 253–257, 2000

    2000

  11. [19]

    Recent progress in high-resolution tactile sensor array: From sensor fabrication to advanced applications,

    R. Wang, S. Hu, W. Zhu, Y . Huang, W. Wang, Y . Li, Y . Yang, J. Yu, and Y . Deng, “Recent progress in high-resolution tactile sensor array: From sensor fabrication to advanced applications,” Progress in Natural Science: Materials International , vol. 33, no. 1, pp. 55–66, 202...

    2023

  12. [20]

    A low-cost modular system of customizable, versatile, and flexible tactile sensor arrays,

    N. Fiedler, P. Ruppel, Y . Jonetzko, N. Hendrich, and J. Zhang, “A low-cost modular system of customizable, versatile, and flexible tactile sensor arrays,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Sep. 2021, p. 17711777. [Online]. Av...

    2021

  13. [21]

    Methods and technologies for the implementation of large- scale robot tactile sensors,

    A. Schmitz, P. Maiolino, M. Maggiali, L. Natale, G. Cannata, and G. Metta, “Methods and technologies for the implementation of large- scale robot tactile sensors,” IEEE Transactions on Robotics , vol. 27, no. 3, p. 389400, Jun. 2011

    2011

  14. [22]

    Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot,

    P. Mittendorfer, E. Yoshida, and G. Cheng, “Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot,” Advanced Robotics , vol. 29, no. 1, p. 5167, Jan. 2015

    2015

  15. [23]

    Tactile-based dual-arm manipu- lation with physical human-robot interaction,

    W. Shen, S. Armleder, and G. Cheng, “Tactile-based dual-arm manipu- lation with physical human-robot interaction,” 2025

    2025

  16. [24]

    Wiresens toolkit: An open-source platform towards accessible wireless tactile sensing,

    D. Murphy, J. Zhu, P. P. Liang, W. Matusik, and Y . Luo, “Wiresens toolkit: An open-source platform towards accessible wireless tactile sensing,” no. arXiv:2412.00247, Dec. 2024, arXiv:2412.00247 [cs]. [Online]. Available: http://arxiv.org/abs/2412.00247

    2024 arXiv

  17. [25]

    Learning humanenvironment interactions using conformal tactile textiles,

    Y . Luo, Y . Li, P. Sharma, W. Shou, K. Wu, M. Foshey, B. Li, T. Palacios, A. Torralba, and W. Matusik, “Learning humanenvironment interactions using conformal tactile textiles,” Nature Electronics, vol. 4, no. 3, p. 193201, Mar. 2021

    2021

  18. [26]

    Recent developments in stretchable and flexible tactile sensors towards piezoresistive systems: A review,

    A. Saxena and K. Patra, “Recent developments in stretchable and flexible tactile sensors towards piezoresistive systems: A review,” Polymers for Advanced Technologies, vol. 35, no. 1, p. e6251, 2024

    2024

  19. [27]

    A readout circuit based on zero potential crosstalk suppression for a large piezore- sistive sensor array: Case study based on a resistor model,

    A. S. Warnakulasuriya, N. Y . Dinushka, A. A. C. Dias, H. P. A. R. Ariyarathna, C. Ramraj, S. Jayasinghe, and A. C. De Silva, “A readout circuit based on zero potential crosstalk suppression for a large piezore- sistive sensor array: Case study based on a resistor model,”IEEE ...

    2021

  20. [28]

    An optimization approach to eliminate crosstalk in zero-potential circuits for reading resistive sensor arrays,

    S. Domnguez-Gimeno, C. Medrano-Snchez, R. Igual-Cataln, J. Martnez- Cesteros, and I. Plaza-Garca, “An optimization approach to eliminate crosstalk in zero-potential circuits for reading resistive sensor arrays,” IEEE Sensors Journal , vol. 23, no. 13, pp. 14 215–14 225, 2023

    2023

  21. [29]

    Scalable fabric tactile sensor arrays for soft bodies,

    N. Day, J. Penaloza, V . J. Santos, and M. D. Killpack, “Scalable fabric tactile sensor arrays for soft bodies,” Journal of Micromechanics and Microengineering, vol. 28, no. 6, p. 064004, Jun. 2018, 13 citations (Semantic Scholar/DOI) [2022-07-07]

    2018

  22. [30]

    L. M. Arruda, I. P. Moreira, U. K. Sanivada, H. Carvalho, and R. Fangueiro, “Development of piezoresistive sensors based on graphene nanoplatelets screen-printed on woven and knitted fabrics: Optimisation of active layer formulation and transversal/longitudinal textile directi...

    2022

  23. [31]

    A. S. Warnakulasuriya, N. Y . Dinushka, A. A. C. Dias, H. P. A. R. Ariyarathna, C. Ramraj, S. Jayasinghe, and A. C. De Silva, “A readout circuit based on zero potential crosstalk suppression for a large piezore- JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 11 sist...

    2015

  24. [32]

    Whole-body multi-contact motion control for humanoid robots based on distributed tactile sensors,

    M. Murooka, K. Fukumitsu, M. Hamze, M. Morisawa, H. Kaminaga, F. Kanehiro, and E. Yoshida, “Whole-body multi-contact motion control for humanoid robots based on distributed tactile sensors,” IEEE Robotics and Automation Letters , vol. 9, no. 11, pp. 10 620–10 627, 2024

    2024

  25. [33]

    A hierarchical framework for robot safety us- ing whole-body tactile sensors,

    S. Jiang and L. L. Wong, “A hierarchical framework for robot safety us- ing whole-body tactile sensors,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 8021–8028

    2024

  26. [34]

    Frequency shapes the quality of tactile percepts evoked through elec- trical stimulation of the nerves,

    E. L. Graczyk, B. P. Christie, Q. He, D. J. Tyler, and S. J. Bensmaia, “Frequency shapes the quality of tactile percepts evoked through elec- trical stimulation of the nerves,” The Journal of Neuroscience , vol. 42, no. 10, p. 20522064, Mar. 2022

    2022

  27. [35]

    Enabling real-time applications in wi-fi networks,

    D. Bankov, E. Khorov, A. Lyakhov, and M. Sandal, “Enabling real-time applications in wi-fi networks,” International Journal of Distributed Sensor Networks, vol. 15, no. 5, p. 1550147719845312, 2019. [Online]. Available: https://doi.org/10.1177/1550147719845312

    2019 doi

  28. [36]

    Tactile sensing over articulated joints with stretchable sensors,

    T. Bhattacharjee, A. Jain, S. Vaish, M. D. Killpack, and C. C. Kemp, “Tactile sensing over articulated joints with stretchable sensors,” in 2013 World Haptics Conference (WHC) . IEEE, 2013, pp. 103–108

    2013

  29. [37]

    Recent progress in flexible and stretchable piezoresistive sensors and their applications,

    J. Li, L. Fang, B. Sun, X. Li, and S. H. Kang, “Recent progress in flexible and stretchable piezoresistive sensors and their applications,” Journal of the Electrochemical Society , vol. 167, no. 3, p. 037561, 2020

    2020

  30. [38]

    Progress in achieving high-performance piezore- sistive and capacitive flexible pressure sensors: A review,

    W. Chen and X. Yan, “Progress in achieving high-performance piezore- sistive and capacitive flexible pressure sensors: A review,” Journal of Materials Science & Technology , vol. 43, pp. 175–188, 2020

    2020

  31. [39]

    Fpga- based tactile sensor suite electronics for real-time embedded processing,

    Oballe-Peinado, J. A. Hidalgo-Lpez, J. Castellanos-Ramos, J. A. Snchez-Durn, R. Navas-Gonzlez, J. Herrn, and F. Vidal-Verd, “Fpga- based tactile sensor suite electronics for real-time embedded processing,” IEEE Transactions on Industrial Electronics , vol. 64, no. 12, pp. 9657...

    2017

  32. [2013]

    His current research interests relate to improving modeling and control for robot manipulation in unstructured and difficult environments

    His areas of expertise include soft robotics, human-robot interaction, controls, mechanics, and perception for robotics and other automated systems. His current research interests relate to improving modeling and control for robot manipulation in unstructured and difficult env...

    2007

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.