Fix branding: ZeroTrustPhysics.com, kruze@zerotrustphysics.com

README.md

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+---
+title: "Zero-Trust Physics: 1000Hz Domain-Randomized Humanoid Grasp & Slip Trajectories (ZTP-TSA-v1)"
+dataset_info:
+  features:
+    - name: episode_id
+      dtype: int64
+    - name: timestamp_sec
+      dtype: float64
+    - name: mass_kg
+      dtype: float64
+    - name: true_static_mu
+      dtype: float64
+    - name: actual_normal_force_n
+      dtype: float64
+    - name: slip_velocity_mps
+      dtype: float64
+    - name: slip_angular_velocity_radps
+      dtype: float64
+    - name: slip_displacement_mm
+      dtype: float64
+    - name: rot_slip_displacement_deg
+      dtype: float64
+    - name: action_commanded_force_n
+      dtype: float64
+    - name: estimated_mu
+      dtype: float64
+    - name: safety_margin
+      dtype: float64
+    - name: micro_slip_detected
+      dtype: bool
+    - name: macro_slip_detected
+      dtype: bool
+    - name: rotational_slip_detected
+      dtype: bool
+    - name: taxel_0_normal_n
+      dtype: float64
+    # Note: Includes taxel_0 to taxel_15 normal, shear_x, and shear_y forces (64 features total)
+  splits:
+    - name: train
+      num_bytes: 224000000
+      num_examples: 438605
+  download_size: 12270000
+  dataset_size: 224000000
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: haptic_trajectories.parquet
+tags:
+  - robotics
+  - reinforcement-learning
+  - tactile-sensing
+  - haptic-feedback
+  - humanoid-manipulation
+  - zero-trust-physics
+  - aegis-os
+  - domain-randomization
+---
+
+# 🤖 Zero-Trust Physics: 1000Hz Dexterous Grasp & Tactile Slip Trajectories (ZTP-TSA-v1)
+
+[![Domain](https://img.shields.io/badge/Website-ZeroTrustPhysics.com-blue?style=flat-square)](https://ZeroTrustPhysics.com)
+[![Maintainer](https://img.shields.io/badge/Lead--Architect-Kruze-purple?style=flat-square)](mailto:kruze@zerotrustphysics.com)
+
+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)
+```
+
+---
+
+## 📊 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.
+
+---
+
+## 🏎️ 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.*
+
+---
+
+## 🛠️ Loading the Dataset
+
+### Python (using Hugging Face `datasets`)
+```python
+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())
+```
+
+---
+
+## 🔬 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:
+$$M_z = \sum_{i=0}^{15} \left( dx_i \cdot \text{shear\_y}_i - dy_i \cdot \text{shear\_x}_i \right)$$
+
+### 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 ($\alpha = 0.05$):
+$$\mu_{s, \text{new}} = \mu_{s, \text{old}} \cdot (1 - \alpha) + \mu_{\text{measured}, \text{avg}} \cdot \alpha$$
+
+---
+
+## 🔒 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](https://github.com/ZeroTrustPhysics/Spectrum/tree/main/ztp-dexterous-hand).