spiderpilot89/humanoid-tactile-slip-reflex-1000hz

🤖 Zero-Trust Physics: 1000Hz Dexterous Grasp & Tactile Slip Trajectories (ZTP-TSA-v1)

This dataset contains high-fidelity 1000Hz timeseries telemetry representing dynamic humanoid robotic grasps under translational and torsional slip disturbances. The data was generated by the Zero-Trust Physics Tactile Slip Auditor (ZTP-TSA), a zero-dependency, FPU-optimized, no-std Rust solver.

⚡ Why This Dataset is Different

Standard robotic datasets capture joint states or RGB images at 30-60Hz. This dataset captures tactile pressure and shear distribution vectors at 1000Hz (1.0ms resolution).

It exposes the physical transition from micro-slip (local tangential sliding of boundary contact points) to macro-slip (global translation sliding) and rotational slip (circumferential slipping under torsional torque), alongside real-time reflex force corrections executed by an edge microcontroller solver running at FPU speeds.

       [4x4 Tactile Sensor Array] (Normal & Shear x/y at 1000Hz)
                    │
                    ▼
     [no-std Rust Solver: ZTP-TSA] (Runs in < 10 microseconds)
                    │
       ┌────────────┴────────────┐
       ▼                         ▼
[Friction Estimation]      [Reflex Correction]
 (Adapts mu_s in 5ms)     (Arrests slip in < 2ms)

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📊 Dataset Schema & Columns

Each row in the dataset corresponds to a single 1.0 millisecond control cycle ($dt = 0.001\text{ s}$). The schema is structured for offline Reinforcement Learning (RL), Behavior Cloning (BC), or VLA (Vision-Language-Action) haptic policy pretraining.

1. Episode / Metadata

• timestamp_sec (float64): Timestamp of the control cycle since episode start.
• scenario (string): The physical environment parameters:
  • NOMINAL: $0.8\text{ kg}$ object, dry tactile pad ($\mu_s = 0.60$).
  • HEAVY_LOAD: $1.0\text{ kg}$ object, high acceleration load ($\mu_s = 0.60$).
  • OILED_SURFACE: $0.4\text{ kg}$ object, sudden low-friction oil patch ($\mu_s = 0.25 \rightarrow 0.10$).

2. Ground-Truth Physics State (state)

• mass_kg (float64): Mass of the grasped object.
• actual_normal_force_n (float64): Actual normal force exerted by the actuator finger pad.
• slip_velocity_mps (float64): Linear slip velocity relative to the finger pad (meters/sec).
• slip_angular_velocity_radps (float64): Rotational slip velocity (radians/sec).
• slip_displacement_mm (float64): Cumulative linear slip distance (millimeters).
• rot_slip_displacement_deg (float64): Cumulative rotational slip angle (degrees).
• static_friction_coeff / dynamic_friction_coeff (float64): True physical friction coefficients.

3. High-Density Tactile Sensor Array (sensor)

A coordinate-mapped $4 \times 4$ tactile matrix centered at index $(1.5, 1.5)$. For each of the 16 taxels ($i \in [0, 15]$):

• taxel_{i}_normal_n (float64): Perpendicular normal pressure force.
• taxel_{i}_shear_x_n (float64): Tangential shear force along the contact grid horizontal axis.
• taxel_{i}_shear_y_n (float64): Tangential shear force along the contact grid vertical axis.

4. Reflex Controller Action (reflex)

• action_commanded_force_n (float64): Commanded target normal force output by the ZTP-TSA controller.
• estimated_mu (float64): Real-time friction coefficient ($\mu_s$) computed by the running observer.
• safety_margin (float64): Friction cone safety margin ($1.0 = \text{no shear}$, $0.0 = \text{imminent slip}$).
• micro_slip_detected (bool): Flagged when boundary contact taxels exceed the local friction limit.
• macro_slip_detected (bool): Flagged when global linear slip is detected.
• rotational_slip_detected (bool): Flagged when torsional moment exceeds the contact boundary limit.

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🏎️ Physical Benchmarks & Catch Latencies

The underlying Rust FFI engine stabilizes all three scenarios in under 2.0ms of slip onset.

Scenario	Object Mass	Start Force	Friction Envelope	Catch Latency	Final Slip	Status
NOMINAL	$0.8\text{ kg}$	$10.0\text{ N}$	$\mu_s = 0.60$	1.00 ms	$3.85\text{ mm}$	Secured
HEAVY LOAD	$1.0\text{ kg}$	$12.0\text{ N}$	$\mu_s = 0.60$	1.00 ms	$15.39\text{ mm}$	Secured
OILED SURFACE	$0.4\text{ kg}$	$14.0\text{ N}$	$\mu_s = 0.25 \rightarrow 0.10$	1.00 ms	$9.48\text{ mm}$	Secured

Note: Latency calculations include a realistic $1.5\text{ ms}$ physical actuator lag ($\tau = 0.0015\text{ s}$) matching direct-drive humanoid motor dynamics.

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🛠️ Loading the Dataset

Python (using Hugging Face datasets)

from datasets import load_dataset
import pandas as pd

# Stream or download the haptic dataset
dataset = load_dataset("spiderpilot89/humanoid-tactile-slip-reflex-1000hz", split="train")

# Convert to Pandas DataFrame
df = dataset.to_pandas()

# Filter for the oiled surface patch scenario
oiled_data = df[df["scenario"] == "OILED_SURFACE"]

# View tactile array shear vector for the first taxel
print(oiled_data[["timestamp_sec", "taxel_0_normal_n", "taxel_0_shear_x_n", "taxel_0_shear_y_n"]].head())

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🔬 Mathematical Formulations

1. Coordinate-Mapped Torsional Moment

The solver maps each taxel index $i$ to a 2D physical spatial vector $(dx_i, dy_i)$ relative to the array center. The net torsional moment $M_z$ is computed as: $$M_z=\sum _{i=0}^{15}(d{x}_i\cdot {\text{shear\_y}}_i-d{y}_i\cdot {\text{shear\_x}}_i)M\_z\; =\; \backslash sum\_\{i=0\}^\{15\}\; \backslash left(\; dx\_i\; \backslash cdot\; \backslash text\{shear\backslash \_y\}\_i\; -\; dy\_i\; \backslash cdot\; \backslash text\{shear\backslash \_x\}\_i\; \backslash right)$$

2. Adaptive Friction Observer

When boundary taxels begin to slip, their tangential-to-normal force ratio is calculated. The system adapts its friction limits dynamically using a first-order low-pass filter ($\alpha = 0.05$): $${\mu }_{s,\text{new}}={\mu }_{s,\text{old}}\cdot (1-\alpha )+{\mu }_{\text{measured},\text{avg}}\cdot \alpha \backslash mu\_\{s,\; \backslash text\{new\}\}\; =\; \backslash mu\_\{s,\; \backslash text\{old\}\}\; \backslash cdot\; (1\; -\; \backslash alpha)\; +\; \backslash mu\_\{\backslash text\{measured\},\; \backslash text\{avg\}\}\; \backslash cdot\; \backslash alpha$$

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🔒 Cryptographic Audit & Aegis OS Seal

Every simulation run generates a cryptographic proof sealed by Aegis OS. The integrity hash protects the simulation manifest from tampering, ensuring that the control latency parameters presented match real execution cycles:

• Domain ID: humanoid_dexterity
• Sovereignty Seal (SHA256): ea0fd0d7c71d6f1f4405a3064619a9b71a2e9cb72b38f8702c2e0a02ef496b7a
• ICP Mainnet Registry: Verification is anchored to the Internet Computer protocol ledger (when AEGIS_NETWORK=ic).

For the FFI Rust headers and Python bindings, visit the Zero Trust Physics GitHub repository.