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diffusion_pusht

Robotics
Transformers
Safetensors
diffusion-policy
model_hub_mixin
pytorch_model_hub_mixin
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@@ -30,7 +30,7 @@ The model was evaluated on the `PushT` environment from [gym-pusht](https://gith
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  - Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
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  - Success: whether or not the maximum overlap is at least 95%.
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- Here are the metrics for 500 episodes worth of evaluation. For the succes rate we add and 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).
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  <blank>|Ours|Theirs
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  -|-|-
 
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  - Maximum overlap with target (seen as `eval/avg_max_reward` in the charts above). This ranges in [0, 1].
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  - Success: whether or not the maximum overlap is at least 95%.
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+ 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).
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  <blank>|Ours|Theirs
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  -|-|-