skill-forge-peginsert-v0 / README.md

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metadata

pretty_name: EXOKERN Skill v0 - Peg Insertion with Force/Torque
license: cc-by-nc-4.0
pipeline_tag: robotics
library_name: pytorch
tags:
  - robotics
  - diffusion-policy
  - force-torque
  - contact-rich
  - manipulation
  - insertion
  - isaac-lab
  - forge
  - franka
  - panda
  - simulation
  - imitation-learning
  - lerobot
  - physical-ai
datasets:
  - EXOKERN/contactbench-forge-peginsert-v0
metrics:
  - success_rate
  - avg_contact_force_n
  - peak_contact_force_n
model-index:
  - name: EXOKERN Skill v0 - Peg Insertion (full_ft)
    results:
      - task:
          type: robotics
          name: Peg insertion
        dataset:
          name: EXOKERN ContactBench v0
          type: EXOKERN/contactbench-forge-peginsert-v0
        metrics:
          - type: success_rate
            value: 100
            name: Success Rate (%)
          - type: avg_contact_force_n
            value: 3.2
            name: Average Contact Force (N)
          - type: peak_contact_force_n
            value: 10.5
            name: Peak Contact Force (N)

EXOKERN Skill v0 - Peg Insertion with Force/Torque

skill-forge-peginsert-v0 is the clean baseline skill release in the EXOKERN catalog. The repository contains a paired policy comparison on the fixed-condition EXOKERN ContactBench v0 dataset:

full_ft_best_model.pt: primary checkpoint with 22D observations, including 6-axis force/torque input
no_ft_best_model.pt: ablation baseline with the same architecture but without force/torque input

The main value of this release is not just success rate. It is a controlled reference point for measuring how force/torque sensing changes contact quality on a simple assembly task.

Quick Facts

Item	Value
Task	Peg insertion in simulation
Dataset	EXOKERN/contactbench-forge-peginsert-v0
Simulator	NVIDIA Isaac Lab (Isaac Sim 4.5)
Robot	Franka Emika Panda
Architecture	TemporalUNet1D diffusion policy
Parameters	71.3M
Observation horizon	10 frames
Prediction / execution horizon	16 / 8 actions
Primary checkpoint	`full_ft_best_model.pt`
Included ablation	`no_ft_best_model.pt`

Primary Benchmark

The Hub metadata for this repo tracks the primary full_ft checkpoint. The full repo includes the paired no_ft ablation for comparison.

Checkpoint	Success Rate	Avg Contact Force (N)	Peak Force (N)
`full_ft`	100.0	3.2 +/- 0.5	10.5 +/- 0.4
`no_ft`	100.0	5.2 +/- 0.1	12.1 +/- 0.3

Figure: published seed-wise benchmark summary for the fixed-condition baseline release.

Per-seed average contact force:

Seed	`full_ft`	`no_ft`	Reduction with F/T
42	3.7 N	5.3 N	30.4%
123	3.4 N	5.0 N	32.3%
7	2.5 N	5.2 N	52.0%

Takeaway: on this controlled baseline, force/torque input preserved success while substantially reducing contact force.

Architecture

This release uses a 1D Temporal U-Net diffusion policy with FiLM-style conditioning from the observation history and diffusion timestep.

Component	Value
Action dimension	7
Observation dimensions	22 (`full_ft`) / 16 (`no_ft`)
Diffusion training steps	100
DDIM inference steps	16
Base channels	256
Channel multipliers	(1, 2, 4)
Normalization	Min-max to `[-1, 1]`

Repository Contents

File	Description
`full_ft_best_model.pt`	Best checkpoint with force/torque input
`no_ft_best_model.pt`	Best checkpoint without force/torque input
`inference.py`	Self-contained inference helper and model definition
`config.yaml`	Training, dataset, and environment configuration
`training_curve_full_ft_seed42.png`	Training curve for `full_ft`, seed 42
`training_curve_full_ft_seed123.png`	Training curve for `full_ft`, seed 123
`training_curve_full_ft_seed7.png`	Training curve for `full_ft`, seed 7
`training_curve_no_ft_seed42.png`	Training curve for `no_ft`, seed 42
`training_curve_no_ft_seed123.png`	Training curve for `no_ft`, seed 123
`training_curve_no_ft_seed7.png`	Training curve for `no_ft`, seed 7

Usage

Reproduce evaluation with `exokern-eval`

pip install exokern-eval

wget https://huggingface.co/EXOKERN/skill-forge-peginsert-v0/resolve/main/full_ft_best_model.pt

exokern-eval \
  --policy full_ft_best_model.pt \
  --env Isaac-Forge-PegInsert-Direct-v0 \
  --episodes 100

Load the repo helper locally

import os
import sys

from huggingface_hub import snapshot_download

repo_dir = snapshot_download(
    repo_id="EXOKERN/skill-forge-peginsert-v0",
    allow_patterns=["*.pt", "inference.py"],
)
sys.path.insert(0, repo_dir)

from inference import DiffusionPolicyInference

policy = DiffusionPolicyInference(
    os.path.join(repo_dir, "full_ft_best_model.pt"),
    device="cpu",
)

policy.add_observation([0.0] * 22)
actions = policy.get_actions()
print(len(actions))

Training And Evaluation Setup

Item	Value
Train / val split	85% / 15% by episode
Epochs	300
Batch size	256
Optimizer	AdamW, `lr=1e-4`, `weight_decay=1e-4`
LR schedule	Cosine annealing to `1e-6`
EMA decay	0.995
Seeds	42, 123, 7
Physics rate	120 Hz
Control rate	15 Hz
Domain randomization	Disabled in this release

Related Work

FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
Factory: Fast Contact for Robotic Assembly

Citation

@misc{exokern_skill_peginsert_v0_2026,
  title        = {EXOKERN Skill v0: Peg Insertion with Force/Torque},
  author       = {{EXOKERN}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/EXOKERN/skill-forge-peginsert-v0}},
  note         = {Paired full_ft and no_ft diffusion-policy checkpoints}
}

Security Note

The checkpoints in this repo are PyTorch pickles. Load them only in a trusted or isolated environment after reviewing the repository contents.

Limitations

Simulation only. This release does not claim real-robot readiness.
The task is a relatively simple peg insertion setting with fixed conditions.
Results should not be generalized to harder contact tasks without additional evidence.
The repo exposes paired checkpoints for research comparison; the intended production-style reference in this repo is full_ft_best_model.pt.

Related Resources

Dataset: EXOKERN/contactbench-forge-peginsert-v0
Domain-randomized successor: EXOKERN/skill-forge-peginsert-v0.1.1
Evaluation CLI: github.com/Exokern/exokern_eval
Organization page: huggingface.co/EXOKERN