Update README card

README.md

@@ -3,6 +3,7 @@ pretty_name: "EXOKERN Skill v0.1.1 - Robust Peg Insertion Under Domain Randomiza
 license: cc-by-nc-4.0
 pipeline_tag: robotics
 library_name: pytorch
+thumbnail: "https://huggingface.co/EXOKERN/skill-forge-peginsert-v0.1.1/resolve/main/assets/preview.png"
 tags:
   - robotics
   - diffusion-policy
@@ -52,6 +53,8 @@ model-index:
 
 This release should be read as a robustness benchmark first. Both policies remain successful under severe domain randomization, and the repo is valuable precisely because it makes the mixed result on force reduction explicit.
 
+![EXOKERN skill v0.1.1 preview](https://huggingface.co/EXOKERN/skill-forge-peginsert-v0.1.1/resolve/main/assets/preview.png)
+
 ## Quick Facts
 
 | Item | Value |
@@ -71,7 +74,7 @@ This release should be read as a robustness benchmark first. Both policies remai
 
 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) | Avg Episode Time (s) |
+| Checkpoint | Success Rate | Avg Contact Force (N) | Peak Contact Force (N) | Avg Episode Time (s) |
 | --- | ---: | ---: | ---: | ---: |
 | `full_ft` | 100.0 | 3.67 +/- 0.45 | 10.63 | 25.63 |
 | `no_ft` | 100.0 | 3.37 +/- 0.06 | 10.33 | 25.73 |
@@ -107,7 +110,7 @@ Interpretation:
 
 This release uses the same 1D Temporal U-Net diffusion policy family as v0.
 
-![Architecture](architecture.png)
+![Architecture](https://huggingface.co/EXOKERN/skill-forge-peginsert-v0.1.1/resolve/main/architecture.png)
 
 | Component | Value |
 | --- | --- |
@@ -173,7 +176,6 @@ policy = DiffusionPolicyInference(
     device="cpu",
 )
 
-# full_ft expects a 22D observation vector
 policy.add_observation([0.0] * 22)
 actions = policy.get_actions()
 print(len(actions))
@@ -193,6 +195,24 @@ print(len(actions))
 | Control rate | 15 Hz |
 | Domain randomization | Enabled in the training dataset |
 
+## Related Work
+
+- FORGE: [Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty](https://arxiv.org/abs/2408.04587)
+- Diffusion Policy: [Visuomotor Policy Learning via Action Diffusion](https://arxiv.org/abs/2303.04137)
+- Factory: [Fast Contact for Robotic Assembly](https://arxiv.org/abs/2205.03532)
+
+## Citation
+
+```bibtex
+@misc{exokern_skill_peginsert_v011_2026,
+  title        = {EXOKERN Skill v0.1.1: Robust Peg Insertion Under Domain Randomization},
+  author       = {{EXOKERN}},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/EXOKERN/skill-forge-peginsert-v0.1.1}},
+  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.