Upload policy weights, train config and readme

README.md

@@ -1,3 +1,62 @@
----
-license: apache-2.0
----
+---
+datasets: ISdept/piper_arm
+library_name: lerobot
+license: apache-2.0
+model_name: diffusion
+pipeline_tag: robotics
+tags:
+- diffusion
+- lerobot
+- robotics
+---
+
+# Model Card for diffusion
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+[Diffusion Policy](https://huggingface.co/papers/2303.04137) treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
+
+
+This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
+See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
+
+---
+
+## How to Get Started with the Model
+
+For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
+Below is the short version on how to train and run inference/eval:
+
+### Train from scratch
+
+```bash
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/<dataset> \
+  --policy.type=act \
+  --output_dir=outputs/train/<desired_policy_repo_id> \
+  --job_name=lerobot_training \
+  --policy.device=cuda \
+  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
+  --wandb.enable=true
+```
+
+_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
+
+### Evaluate the policy/run inference
+
+```bash
+lerobot-record \
+  --robot.type=so100_follower \
+  --dataset.repo_id=<hf_user>/eval_<dataset> \
+  --policy.path=<hf_user>/<desired_policy_repo_id> \
+  --episodes=10
+```
+
+Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
+
+---
+
+## Model Details
+
+- **License:** apache-2.0

model.safetensors

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train_config.json

@@ -170,13 +170,13 @@
         "scheduler_name": "cosine",
         "scheduler_warmup_steps": 500
     },
-    "output_dir": "outputs/train/output",
+    "output_dir": "outputs/train/output_diff",
     "job_name": "pick_and_place",
     "resume": false,
     "seed": 1000,
     "num_workers": 4,
-    "batch_size": 8,
-    "steps": 5000,
+    "batch_size": 16,
+    "steps": 10000,
     "eval_freq": 20000,
     "log_freq": 200,
     "save_checkpoint": true,