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@@ -4,7 +4,7 @@ license: apache-2.0
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  language:
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  - en
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  base_model:
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- - SberRoboticsCenter/GreenVLA-5b-base
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  pipeline_tag: robotics
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  tags:
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  - robotics
@@ -20,7 +20,7 @@ tags:
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  datasets:
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  - IPEC-COMMUNITY/bridge_orig_lerobot
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  model-index:
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- - name: GreenVLA-5b-R2-bridge
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  results:
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  - task:
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  type: robotics
@@ -39,7 +39,7 @@ model-index:
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  <div align="center">
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- # GreenVLA-5b-R2-bridge
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  ### RL-Aligned VLA for Bridge (WidowX)
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@@ -55,9 +55,9 @@ model-index:
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  ## Overview
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- **GreenVLA-5b-R2-bridge** is the R2 (RL-aligned) checkpoint of the [Green-VLA](https://arxiv.org/abs/2602.00919) family, fine-tuned on the [Bridge](https://huggingface.co/datasets/IPEC-COMMUNITY/bridge_orig_lerobot) dataset for the WidowX robot arm joined with additional trajectories collected in SimplerEnv Bridge environments. Trajectory collection and RL fine-tuning were conducted according to the Trajectory Optimization approach described in the [technical report](https://arxiv.org/abs/2602.00919).
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- Starting from [GreenVLA-5b-base](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base), this model went through both R1 (supervised fine-tuning) and R2 (RL policy alignment) stages, resulting in significant performance gains over behavior cloning alone.
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  ## Evaluation
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@@ -89,7 +89,7 @@ Evaluated on **SimplerEnv WidowX (Bridge)** benchmark.
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  | | Details |
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  |---|---|
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- | **Base checkpoint** | [GreenVLA-5b-base](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base) |
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  | **Stage** | R2 — RL policy alignment |
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  | **Method** | Trajectory optimization (SFT + RL on collected trajectories) |
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  | **Dataset** | [IPEC-COMMUNITY/bridge_orig_lerobot](https://huggingface.co/datasets/IPEC-COMMUNITY/bridge_orig_lerobot) + SimplerEnv rollouts |
@@ -119,7 +119,7 @@ from lerobot.common.utils.torch_observation import (
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  # 1. Load policy and transforms.
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  policy, input_transforms, output_transforms = load_pretrained_policy(
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- "SberRoboticsCenter/GreenVLA-5b-R2-bridge",
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  data_config_name="bridge",
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  )
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  policy.to("cuda").eval()
@@ -148,16 +148,6 @@ actions = output_transforms(
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  See [`examples/example_inference_bridge.py`](https://github.com/greenvla/GreenVLA/blob/main/examples/example_inference_bridge.py) for the full runnable script with argument parsing.
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- ## Model Family
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-
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- | Model | Stage | Params | Description | Link |
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- |-------|:-----:|:------:|-------------|:----:|
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- | **GreenVLA-2b-base** | Base | 2B | Base pretrained (lightweight) | [Hub](https://huggingface.co/SberRoboticsCenter/GreenVLA-2b-base) |
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- | **GreenVLA-5b-base** | Base | 5B | Base pretrained (recommended) | [Hub](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base) |
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- | **GreenVLA-5b-R1-bridge** | R1 | 5B | Fine-tuned on Bridge (WidowX) | [Hub](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-R1-bridge) |
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- | **GreenVLA-5b-R2-bridge** | R2 | 5B | RL-aligned on Bridge (WidowX) | You are here |
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- | **GreenVLA-5b-R1-fractal** | R1 | 5B | Fine-tuned on Fractal (Google Robot) | [Hub](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-R1-fractal) |
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-
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  ## Citation
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  ```bibtex
 
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  language:
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  - en
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  base_model:
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+ - SberRoboticsCenter/GreenVLA-5b-base-stride-1
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  pipeline_tag: robotics
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  tags:
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  - robotics
 
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  datasets:
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  - IPEC-COMMUNITY/bridge_orig_lerobot
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  model-index:
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+ - name: GreenVLA-5b-stride-1-R2-bridge
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  results:
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  - task:
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  type: robotics
 
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  <div align="center">
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+ # GreenVLA-5b-stride-1-R2-bridge
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  ### RL-Aligned VLA for Bridge (WidowX)
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  ## Overview
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+ **GreenVLA-5b-stride-1-R2-bridge** is the R2 (RL-aligned) checkpoint of the [Green-VLA](https://arxiv.org/abs/2602.00919) family, fine-tuned on the [Bridge](https://huggingface.co/datasets/IPEC-COMMUNITY/bridge_orig_lerobot) dataset for the WidowX robot arm joined with additional trajectories collected in SimplerEnv Bridge environments. Trajectory collection and RL fine-tuning were conducted according to the Trajectory Optimization approach described in the [technical report](https://arxiv.org/abs/2602.00919).
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+ Starting from [GreenVLA-5b-base-stride-1](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base-stride-1), this model went through both R1 (supervised fine-tuning) and R2 (RL policy alignment) stages, resulting in significant performance gains over behavior cloning alone.
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  ## Evaluation
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  | | Details |
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  |---|---|
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+ | **Base checkpoint** | [GreenVLA-5b-base-stride-1](https://huggingface.co/SberRoboticsCenter/GreenVLA-5b-base-stride-1) |
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  | **Stage** | R2 — RL policy alignment |
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  | **Method** | Trajectory optimization (SFT + RL on collected trajectories) |
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  | **Dataset** | [IPEC-COMMUNITY/bridge_orig_lerobot](https://huggingface.co/datasets/IPEC-COMMUNITY/bridge_orig_lerobot) + SimplerEnv rollouts |
 
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  # 1. Load policy and transforms.
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  policy, input_transforms, output_transforms = load_pretrained_policy(
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+ "SberRoboticsCenter/GreenVLA-5b-stride-1-R2-bridge",
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  data_config_name="bridge",
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  )
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  policy.to("cuda").eval()
 
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  See [`examples/example_inference_bridge.py`](https://github.com/greenvla/GreenVLA/blob/main/examples/example_inference_bridge.py) for the full runnable script with argument parsing.
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  ## Citation
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  ```bibtex