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README.md

@@ -1,8 +1,3 @@
----
-license: apache-2.0
-datasets:
-- lerobot/aloha_sim_transfer_cube_human
----
 # Model Card for ACT/AlohaTransferCube
 
 Action Chunking Transformer Policy (as per [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://arxiv.org/abs/2304.13705)) trained for the `AlohaTransferCube` environment from [gym-aloha](https://github.com/huggingface/gym-aloha).
@@ -37,4 +32,4 @@ Success rate for 500 episodes (%) | 87.6 | 68.0
 Beta distribution lower/mean/upper (%) | 86.0 / 87.5 / 88.9 | 65.8 / 67.9 / 70.0
 
 
-The original code was heavily refactored, and some bugs were spotted along the way. The differences in code may account for the difference in success rate. Another possibility is that our simulation environment may use slightly different heuristics to evaluate success (we've observed that success is registered as soon as the second arm's gripper makes antipodal contact with the cube). Finally, one should observe that the in-training evaluation jumps up towards the end of training. This may need further investigation (Is it statistically significant? If so, what is the cause?).
+The original code was heavily refactored, and some bugs were spotted along the way. The differences in code may account for the difference in success rate. Another possibility is that our simulation environment may use slightly different heuristics to evaluate success (we've observed that success is registered as soon as the second arm's gripper makes antipodal contact with the cube). Finally, one should observe that the in-training evaluation jumps up towards the end of training. This may need further investigation (Is it statistically significant? If so, what is the cause?).