Add todo

README.md

@@ -21,7 +21,7 @@ See the [LeRobot library](https://github.com/huggingface/lerobot) (particularly
 
 ## Training Details
 
-Trained with [LeRobot@d747195](https://github.com/huggingface/lerobot/tree/d747195c5733c4f68d4bfbe62632d6fc1b605712).
+Trained with [LeRobot@TODO](https://github.com/huggingface/lerobot/tree/TODO).
 
 The model was trained using [LeRobot's training script](https://github.com/huggingface/lerobot/blob/main/lerobot/scripts/train.py) and with the [aloha_sim_transfer_cube_human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human) dataset, using this command:
 
@@ -42,7 +42,7 @@ This took about 1h45 to train on an Nvida A100.
 
 ## Evaluation
 
-The model was evaluated on the `AlohaTransferCube` environment from [gym-aloha](https://github.com/huggingface/gym-aloha) and compared to a similar model trained with the original [ACT repository](https://github.com/tonyzhaozh/act). Each episode marks a success if the cube is successfully picked by one robot arm and transferred to the other robot arm.
+The model was evaluated on the `AlohaTransferCube` task from [gym-aloha](https://github.com/huggingface/gym-aloha) and compared to a similar model trained with the original [ACT repository](https://github.com/tonyzhaozh/act). Each episode marks a success if the cube is successfully picked by one robot arm and transferred to the other robot arm.
 
 Here are the success rate results for 500 episodes worth of evaluation. The first row is the naive mean.  The second row assumes a uniform prior and computes the beta posterior, then calculates the mean and lower/upper confidence bounds (with a 68.2% confidence interval centered on the mean). The "Theirs" column is for an equivalent model trained on the original ACT repository and evaluated on LeRobot (the model weights may be found in the [`original_act_repo`](https://huggingface.co/lerobot/act_aloha_sim_transfer_cube_human/tree/original_act_repo) branch of this respository). The results of each of the individual rollouts may be found in [eval_info.json](eval_info.json).