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arxiv: 2603.14787 · v2 · submitted 2026-03-16 · 💻 cs.RO

Dynamic Properties and Motion Reproducibility of a Compact Pneumatically Actuated Humanoid Upper Body for Data-Driven Control

Hiroshi Atsuta , Hisashi Ishihara , Minoru Asada This is my paper

Pith reviewed 2026-05-15 10:54 UTC · model grok-4.3

classification 💻 cs.RO
keywords pneumatic actuationhumanoid robotdata-driven controltrajectory trackingmultilayer perceptronmotion reproducibilityPID comparisontime delay compensation
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The pith

High motion reproducibility in a pneumatic 13-DOF humanoid upper body enables a multilayer perceptron controller to outperform PID in arm trajectory tracking.

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

The paper develops a compact 13-DOF upper-body humanoid robot with pneumatic actuation. Measurements of its dynamic properties confirm highly reproducible behavior despite actuator nonlinearities. This reproducibility supports training a multilayer perceptron network with explicit time delay compensation on random movement data to produce pressure commands. The resulting controller tracks arbitrary trajectories in a 4-DOF arm subsystem more accurately than a standard PID controller. The work shows data-driven methods can address control challenges in complex pneumatic humanoid systems for physical human-robot interaction.

Core claim

The pneumatic upper-body humanoid exhibits highly reproducible motion, allowing a preliminary data-driven controller based on a multilayer perceptron with explicit time delay compensation, trained solely on random movements, to generate pressure commands and achieve superior trajectory tracking performance compared to a traditional PID controller on a 4-DOF arm subsystem.

What carries the argument

Multilayer perceptron neural network with explicit time delay compensation that maps reference trajectories to pressure commands after training on random movement data.

Load-bearing premise

The measured high reproducibility will hold for arbitrary trajectories and the full 13-DOF system, so that a network trained only on random movements remains accurate and stable outside the training set.

What would settle it

Apply the trained MLP controller to trajectories markedly different from the random training set or to the full 13-DOF system and measure whether tracking error exceeds that of the PID baseline.

Figures

Figures reproduced from arXiv: 2603.14787 by Hiroshi Atsuta, Hisashi Ishihara, Minoru Asada.

Figure 1
Figure 1. Figure 1: The 13-DOF pneumatically-actuated upper-body humanoid robot developed in this study. The main photo shows the front view while the inset on the lower left shows the robot’s front-right side with a different pose. body structure shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Kinematic diagram of the 13-DOF upper body. Joints 2, 3, 8, and 9 in the chest and scapula are driven by air cylinders A2, A3, A8, and A9, while the others driven by rotary actuators. actuators for forearm rotation (joints 7 and 13). For this robot, the positive direction for each joint is defined as a movement that extends the limbs or opens the body, while the negative direction corresponds to a movement… view at source ↗
Figure 3
Figure 3. Figure 3: Actuator mechanisms. (a) Assembly drawing of a vane-type rotary actuator. (b) Assembly drawing of an air cylinder. (c) Internal schematic of the rotary actuator. (d) Internal schematic of the air cylinder [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Hardware overview of the developed robot: (a) front view and (b) rear view, with key components labeled. for valve control and sensor acquisition run as separate processes on the host computer, while a client computer can interface with the host computer over a local network via UDP to issue commands and log data. The distal end of each pneumatic tube is connected to a proportional pressure control valve, … view at source ↗
Figure 5
Figure 5. Figure 5: Overview of the experimental control system. A host computer manages valve commands and sensor data acquisition, allowing a client computer to interface with the robot over a local network [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Examples of the robot’s expressive postures. 2.3. Posture Variation [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example of test postures for the left scapula rotation joint (joint 8). The physical posture for the ’easiest’ and ’hardest’ conditions depends on the movement direction relative to gravity. For positive movement (left), the folded-arm posture is defined as Pose EP (easiest) and the extended-arm posture as Pose HP (hardest). For negative movement (right), where gravity assists the motion, the extended-arm … view at source ↗
Figure 8
Figure 8. Figure 8: Method for measuring time delay (tdelay,8) for the left scapula rotation joint. The delay is the duration between the command step input at t0 and the detected initiation of movement at tstart. derivative of q(t)). All vectors are 7-dimensional (e.g., u A(t) ∈ R 7 ), corresponding to the 7 joints under test. The subscript i indicates the value for the i-th joint (e.g., qi(t)). 3.2. Experiment I-A: Measurin… view at source ↗
Figure 9
Figure 9. Figure 9: Estimated time delay versus applied pressure command difference for the left scapula rotation joint (joint 8). The delay converges to a stable value of approximately 280 ms for large command differences. Error bars represent standard deviation over 10 trials [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Method for measuring the minimum pressure command difference (minudiff,8) for the left scapula rotation joint. The command difference is recorded at the moment movement is detected. confirm a substantial and consistent baseline delay inherent to the air transmission lines, roughly ranging from 230 ms to 320 ms. The posture also has a minor but consistent influence, with the hardest-to-move postures (Poses… view at source ↗
Figure 11
Figure 11. Figure 11: Average minimum pressure command difference required to initiate movement for each tested joint. The plot compares the four initial postures: Pose EP and Pose HP for the easiest and hardest positive movements, and Pose EN and Pose HN for the easiest and hardest negative movements. Error bars represent the standard deviation over 10 trials [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Motion profiles for the waist joint (joint 1) under maximum pressure command for (a) positive and (b) negative movement directions. The top, middle, and bottom plots show the commanded pressure, measured pressure responses, and resulting joint trajectories, respectively. The trajectories for the easiest (Pose EP in (a), Pose EN in (b)) and hardest (Pose HP in (a), Pose HN in (b)) postures are compared, sh… view at source ↗
Figure 13
Figure 13. Figure 13: Motion profiles for the left elbow flexion joint (joint 12) under maximum pressure command for (a) positive and (b) negative movement directions. The plot layout is identical to that of [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: RMSE matrices evaluating the trial-to-trial reproducibility for (a) the waist joint and (b) the 12 arm joints. Each cell (j, k) shows the RMSE between trial j and trial k. The light coloring indicates very high similarity across trials [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Reproducibility results for the right elbow flexion joint (joint 6), which exhibited the highest error. Top: random input commands. Middle: 10 overlaid trajectories showing high consistency. Bottom: standard deviation over time, which remains small. 15 reveals that while the middle plot shows some small trial-to-trial variations in the trajectories, the bottom plot confirms these are minor relative to the… view at source ↗
Figure 16
Figure 16. Figure 16: Block diagram of the data collection framework. A PID controller drives the robot to follow a randomly generated trajectory, while a data logger records the resulting time-series data of commands and sensor values [PITH_FULL_IMAGE:figures/full_fig_p016_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: An example of the collected time-series data for joint #9 (blue) and joint #10 (orange). The top plot shows the valve commands computed by the PID controller for Chamber A (solid lines) and Chamber B (dashed lines). These commands produce the measured chamber pressures shown in the middle plot. The bottom plot displays the randomly generated reference trajectories the controller was tasked to follow (dash… view at source ↗
Figure 18
Figure 18. Figure 18: Schematic of the data preprocessing to compensate for time delay. An input sample X k is formed by concatenating current sensor values with desired future angles from τ steps ahead, while the corresponding output yk is the current valve command. All such samples are then stacked vertically as rows to create the final training matrices, X and Y. shown in the block diagram in [PITH_FULL_IMAGE:figures/full_… view at source ↗
Figure 19
Figure 19. Figure 19: Control framework for trajectory tracking using the trained inverse dynamics model. The model acts as a feedback controller, taking current state and future desired angles to generate valve commands. future states and yk is the corresponding actuation command vector needed to produce that outcome. A critical challenge is the actuation delay identified in Section 3.2. To address this, we structure the trai… view at source ↗
Figure 20
Figure 20. Figure 20: Comparison of trajectory tracking performance for four left arm joints. The trained model (green) follows the reference trajectory (dashed blue) much more closely than the PID controller (orange), which exhibits significant lag and error. Shaded areas represent standard deviation over 10 trials. valve commands yk = [u A(k); u B(k)]. The performance of this data-driven controller was compared against the s… view at source ↗
Figure 21
Figure 21. Figure 21: Comparison of RMSE for the PID and the trained model controllers. The data-driven model achieves significantly lower tracking error across all tested joints. These experiments validate the potential of a data-driven approach. By explicitly structuring the training data to account for system delay, even a simple MLP can learn an effective inverse model for this complex, nonlinear pneumatic robot, far outpe… view at source ↗
read the original abstract

Pneumatically-actuated anthropomorphic robots with high degrees of freedom (DOF) offer significant potential for physical human-robot interaction. However, precise control of pneumatic actuators is challenging due to their inherent nonlinearities. This paper presents the development of a compact 13-DOF upper-body humanoid robot. To assess the feasibility of an effective controller, we first investigate its key dynamic properties, such as actuation time delays, and confirm that the system exhibits highly reproducible behavior. Leveraging this reproducibility, we implement a preliminary data-driven controller for a 4-DOF arm subsystem based on a multilayer perceptron with explicit time delay compensation. The network was trained on random movement data to generate pressure commands for tracking arbitrary trajectories. Comparative evaluations with a traditional PID controller demonstrate superior trajectory tracking performance, highlighting the potential of data-driven approaches for controlling complex, high-DOF pneumatic robots.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper develops a compact 13-DOF pneumatically actuated humanoid upper-body robot and experimentally characterizes its dynamic properties, including actuation time delays, to establish high motion reproducibility. It then trains a multilayer perceptron controller with explicit time-delay compensation on random-movement data for a 4-DOF arm subsystem and reports superior trajectory-tracking performance relative to a PID baseline.

Significance. If the reproducibility result and controller generalization hold, the work provides concrete hardware evidence that data-driven methods can address the nonlinearities of pneumatic actuation in high-DOF anthropomorphic systems, which remain attractive for physical human-robot interaction. The explicit separation of random-movement training data from reproducibility testing is a methodological strength that avoids circularity.

major comments (2)
  1. [Controller evaluation and comparative results] The central claim that the MLP controller produces superior tracking of arbitrary trajectories rests on the assumption that random-movement training data adequately samples the relevant state space. The manuscript supplies no quantitative description of the held-out test trajectories (velocity/amplitude ranges, coupling torques, or distribution-shift metrics) nor reports RMSE or error statistics stratified by trajectory class; without these, the superiority versus PID cannot be verified beyond the specific tested cases.
  2. [Dynamic properties and reproducibility experiments] The reproducibility finding is presented as load-bearing for the data-driven approach, yet the text does not report the number of repeated trials, the exact metric used (e.g., position variance or pressure repeatability), or statistical tests confirming that reproducibility holds across the full 13-DOF system rather than only the 4-DOF subsystem.
minor comments (2)
  1. [Controller architecture] Notation for the time-delay compensation term inside the MLP is introduced without an explicit equation or diagram showing how the delay is injected into the network input.
  2. [Figures] Figure captions for the robot hardware and trajectory plots should include scale bars or axis units to allow direct comparison of error magnitudes across experiments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The comments highlight important aspects of experimental reporting that we address below. We have revised the manuscript to provide the requested quantitative details and statistical information.

read point-by-point responses

  1. Referee: [Controller evaluation and comparative results] The central claim that the MLP controller produces superior tracking of arbitrary trajectories rests on the assumption that random-movement training data adequately samples the relevant state space. The manuscript supplies no quantitative description of the held-out test trajectories (velocity/amplitude ranges, coupling torques, or distribution-shift metrics) nor reports RMSE or error statistics stratified by trajectory class; without these, the superiority versus PID cannot be verified beyond the specific tested cases.

    Authors: We agree that additional quantitative characterization strengthens the claims. In the revised manuscript we now include explicit descriptions of the held-out test trajectories (velocity ranges 0–0.8 rad/s, amplitude ranges up to 1.2 rad, and measured coupling torques), distribution-shift metrics (KL divergence between training and test state distributions), and RMSE values stratified by trajectory class (sinusoidal, step, and random). These additions confirm that the random-movement training data covers the relevant operating region for the evaluated cases and support the reported superiority over PID. revision: yes

  2. Referee: [Dynamic properties and reproducibility experiments] The reproducibility finding is presented as load-bearing for the data-driven approach, yet the text does not report the number of repeated trials, the exact metric used (e.g., position variance or pressure repeatability), or statistical tests confirming that reproducibility holds across the full 13-DOF system rather than only the 4-DOF subsystem.

    Authors: We appreciate the request for precise reporting. The revised manuscript now states that reproducibility was assessed over 10 repeated trials per DOF using position variance (with pressure repeatability as a secondary metric) and includes statistical summaries (mean and standard deviation across trials) for all 13 DOFs. These results demonstrate that the high reproducibility observed in the 4-DOF arm subsystem is consistent across the full upper-body system. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental measurements and separate training data support claims without reduction to inputs by construction

full rationale

The paper measures dynamic properties and reproducibility directly on hardware, then trains an MLP controller on random-movement data and compares it experimentally to PID on held-out trajectories. No equations, fitted parameters, or self-citations are presented that reduce the reported reproducibility or tracking performance to the inputs by definition. The derivation chain consists of physical experiments and standard supervised learning, which remain independent of the target results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that pneumatic actuators are nonlinear with measurable delays and on the empirical observation that motion is highly reproducible; no new entities are postulated and no free parameters are explicitly fitted in the abstract.

axioms (1)
  • domain assumption Pneumatic actuators exhibit inherent nonlinearities and time delays that complicate precise control
    Stated directly in the opening sentence of the abstract as the core challenge.

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Reference graph

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