EXOKERN — Pre-trained Manipulation Skills for Physical AI

Contact-Foundation Model for Assembly · Force/Torque-validated · Open & Reproducible

We build pre-trained robotic manipulation skills for contact-rich tasks — where vision alone fails and precise force control matters. Every skill ships with full F/T ablation studies, multi-seed evaluation, and standardized benchmarks.

Vision: Building the first open Kontakt-Foundation Model for robotic assembly — multi-task, multi-robot, force-aware.

---

🎯 The Problem

Over 95% of robotic manipulation approaches are vision-only. They fail at contact-rich tasks like insertion, assembly, and snap-fit — where sub-Newton force control is the difference between success and broken parts.

Training a robot for a new contact task takes weeks of engineering. Validating it works safely takes even longer. And the resulting policy only works on one robot.

💡 What EXOKERN Provides

Skills — Pre-trained policies with force/torque awareness

v0.2 Architecture (in development):

• iDP3 Egocentric Point Cloud (1024×3) — no camera calibration needed
• 9D Task-Space Actions (3 Pos + 6D Continuous Rotation) — robot-agnostic, no singularities
• F/T in EE-Frame — consistent force feedback regardless of arm configuration
• First system to combine: Point Cloud + F/T + Diffusion Policy

Datasets — Contact-rich training data with F/T annotations

All datasets: LeRobot v3.0 format · 6-axis F/T wrench · Full state + action pairs · CC-BY-NC-4.0

Tools — Evaluate any manipulation policy

pip install exokern-eval
exokern-eval --policy your_checkpoint.pt --env Isaac-Forge-PegInsert-Direct-v0 --episodes 100

• PyPI: exokern-eval
• GitHub: github.com/Exokern/exokern_eval

---

📊 Validated Results — Peg Insert v0

Evaluated across 3 random seeds × 2 conditions × 100 episodes = 600 rollouts total:

Key Finding: F/T sensing consistently reduces contact forces across all architectures — critical for real-world deployment where excessive force = broken parts.

---

🧬 Skill Quality Pyramid

Every skill has a defined quality level — we don't pretend sim-only is production-ready:

Level 3: Real-World Validated     → v2 (2027)     → Production-ready
Level 2: Multi-Robot Sim          → v1 (Q4 2026)  → Cross-platform
Level 1: Sim-Validated + OOD      → v0.2 (Q2 2026) → Community
Level 0: Proof of Concept         → v0 (DONE ✅)  → Research baseline

---

🔧 What Makes EXOKERN Different

---

🏗️ Built With

• NVIDIA Isaac Lab — high-fidelity physics simulation with GPU parallelization
• Diffusion Policy / iDP3 — 71.3M parameter architecture (v0.2: egocentric 3D)
• Franka FR3 (7-DOF) — research-grade robot platform
• LeRobot v3.0 — standardized dataset format for interoperability
• Bota Systems SensONE — 6-axis F/T sensor (simulated with calibrated noise model)

---

🗺️ Roadmap

Building toward a Kontakt-Foundation Model for Assembly — multi-task, multi-robot, force-aware:

---

📜 License

• Code: Apache-2.0
• Model Weights: CC-BY-NC-4.0 (commercial use requires separate license)
• Datasets: CC-BY-NC-4.0

📬 Contact

• 🤗 HuggingFace: huggingface.co/EXOKERN
• 🐙 GitHub: github.com/Exokern
• 📧 Contact: exokern@proton.me

---

EXOKERN — Bridging the Haptic Gap in Robotic Manipulation