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README.md

@@ -8,45 +8,32 @@ tags:
   - forcesight
   - tactile
 ---
-
 # ForceSight — pi0.5 fine-tuned checkpoints
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 Fine-tuned pi0.5 (openpi) policies for ForceSight manipulation tasks.
 All checkpoints are from step 20000, trained on 6/3 data.
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 ## Checkpoints
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 | Folder | Variant | Description |
 |--------|---------|-------------|
 | `pi05_6_3/` | Baseline pi0.5 | Vanilla pi0.5 fine-tune, no tactile. |
 | `encoder_6_3/` | pi0.5 + tactile encoder | Conv-Based encoder for tactile images |
 | `tactile_6_3/` | pi0.5 + tactile | Tactile images are augmented as camera inputs to the VLA model |
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+| `tapvla_6_3/` | pi0.5 + annotation | Tactile sensor data is annotated directly on the VLA images |
 Each folder contains `params/` (orbax weights) and `assets/` (normalization stats — required for inference).
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 ## Setup
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 - **Base model:** pi0.5 (openpi)
 - **Robot:** Franka Emika Panda + Franka Hand
 - **Tasks:** Medicine, Balance, Gear Insertion, Plug Insertion. 
 - **Training:** 20000 steps, 4 A6000 GPUs.
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 ## Loading
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 Download a single checkpoint:
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 ```bash
 hf download mlshehab/forcesight --include "pi05_6_3/*" --local-dir ./forcesight
 ```
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 Load with openpi:
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 ```python
 from openpi.policies import policy_config
 from openpi.training import config
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 cfg = config.get_config("<FILL IN: config name, e.g. pi05_forcesight>")
 policy = policy_config.create_trained_policy(cfg, "./forcesight/pi05_6_3")
 ```
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-> Note: the tactile variants require a custom openpi config/fork. See [openpi](https://github.com/Physical-Intelligence/openpi).
+> Note: the tactile and TAP-VLA variants require a custom openpi config/fork. See [openpi](https://github.com/Physical-Intelligence/openpi).