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README.md

@@ -30,7 +30,7 @@ The model was evaluated on the `PushT` environment from [gym-pusht](https://gith
 - Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
 - Success: whether or not the maximum overlap is at least 95%.
 
-Here are the metrics for 500 episodes worth of evaluation. For the succes rate we add an extra row with confidence bounds. This assumes a uniform prior over success probability and computes the beta posterior, then calculates the mean and lower/upper confidence bounds (with a 68.2% confidence interval centered on the mean).
+Here are the metrics for 500 episodes worth of evaluation. For the succes rate we add an extra row with confidence bounds. This assumes a uniform prior over success probability 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 Diffusion Policy repository and evaluated on LeRobot (the model weights may be found in the [`original_dp_repo`](https://huggingface.co/lerobot/diffusion_pusht/tree/original_dp_repo) branch of this respository).
 
 <blank>|Ours|Theirs
 -|-|-