Add pipeline tag and links to paper, project page, and code

#1
by nielsr HF Staff - opened
Files changed (1) hide show
  1. README.md +7 -2
README.md CHANGED
@@ -3,11 +3,16 @@ tags:
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  - reinforcement-learning
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  - robotics
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  - dexterous-manipulation
 
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  ---
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  # DexPolicy representative checkpoints
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- This repository contains a representative subset of the object-specific policies reported in the DexPolicy paper. The released pairs use the YCB mustard-bottle relocation task and isolate the paper's exploration-scheduling intervention under PPO, GRPO, and FPO.
 
 
 
 
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  ## Checkpoints
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@@ -24,4 +29,4 @@ The PPO and FPO pairs train for approximately 5M environment steps with matched
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  The PPO and GRPO `.zip` archives follow the Stable-Baselines3 checkpoint layout and include the policy, PyTorch variables, optimizer state, and library-version metadata. The FPO `.pt` files are PyTorch training checkpoints for the conditional-flow actor and critic. These policies are object-specific and are not presented as a single cross-object policy.
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- This is a compact representative release rather than the complete multi-object checkpoint suite.
 
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  - reinforcement-learning
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  - robotics
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  - dexterous-manipulation
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+ pipeline_tag: robotics
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  ---
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  # DexPolicy representative checkpoints
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+ This repository contains a representative subset of the object-specific policies reported in the paper [DexPolicy: Scheduled Exploration for Trajectory-Guided Dexterous Manipulation](https://huggingface.co/papers/2610.00360).
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+ Project page: [https://aigeeksgroup.github.io/DexPolicy/](https://aigeeksgroup.github.io/DexPolicy/)
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+ Code: [https://github.com/AIGeeksGroup/DexPolicy](https://github.com/AIGeeksGroup/DexPolicy)
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+
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+ The released pairs use the YCB mustard-bottle relocation task and isolate the paper's exploration-scheduling intervention under PPO, GRPO, and FPO.
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  ## Checkpoints
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  The PPO and GRPO `.zip` archives follow the Stable-Baselines3 checkpoint layout and include the policy, PyTorch variables, optimizer state, and library-version metadata. The FPO `.pt` files are PyTorch training checkpoints for the conditional-flow actor and critic. These policies are object-specific and are not presented as a single cross-object policy.
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+ This is a compact representative release rather than the complete multi-object checkpoint suite.