KO
|
EN
gitlite — search
Search
#javascript
#python
#hacktoberfest
#react
#ai
#typescript
#llm
#go
#golang
#android
#machine-learning
#rust
#deep-learning
#linux
HABIT-policy-training
★ 14
Open GitHub ↗
No description available.
Download README (.md)
Explore Similar Repositories
cpa-grok
:
CLIProxyAPI 反代 Grok 免费号，免费体验 Grok-4.5 模型
skills
:
shanjian skills
Demo_repository_4_test
:
TEST_repository_описание
awesome-generative-ui
:
Developer resources for building with and learning about generative UI
TrafficAnalyzer
:
应急响应流量分析工具，支持pcap、excel、log等多种数据源，同时结合加解密、反编译等多个常用工作流。
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
HABIT-policy-training
?
Download (.md)
# HABIT Policy Training Training code for the two vision-language-action (VLA) policies fine-tuned on the [**HABIT**](https://huggingface.co/datasets/configinc/HABIT) dataset — a large-scale bimanual, human-present robot manipulation dataset in LeRobot v2 format. This repository packages two independent, self-contained training setups, each vendoring its upstream policy codebase and adding a thin HABIT-specific configuration layer. Both have been adapted to run on a **single local machine** (no cluster/orchestration dependencies). | Model | Directory | Upstream | HABIT entrypoint | |---|---|---|---| | **π0.5** (OpenPI) | [`PI05/`](PI05/) | [openpi](https://github.com/Physical-Intelligence/openpi) | `python -m local_training.train_local samples/envs/.env.habit` | | **GR00T N1.6** | [`Gr00t-N1.6/`](Gr00t-N1.6/) | [Isaac GR00T](https://github.com/NVIDIA/Isaac-GR00T) | `bash examples/HABIT/finetune_habit_1gpu.sh` | Each directory has its own README with full setup and usage instructions: - **[`PI05/README.md`](PI05/README.md)** — OpenPI (π0 / π0.5 / π0-FAST) local training. - **[`Gr00t-N1.6/README.md`](Gr00t-N1.6/README.md)** — GR00T N1.6 fine-tuning (see the *Fine-tune HABIT* section). --- ## The HABIT data layout both models consume HABIT is a **bimanual Franka Research 3** dataset. Both training setups read the same LeRobot v2 fields: - **State (14D):** per-arm end-effector Cartesian position (xyz 3D + rotation 3D) + gripper (1D), for the left and right arm. - **Action (14D):** per-arm end-effector **delta action** (7D [xyz 3D, rotation 3D, gripper 1D]), left and right. - **Cameras (robot-side):** `front_view`, `left_wrist_view`, `right_wrist_view`. > The full HABIT dataset additionally ships two human-side camera streams > (`human_front_view`, `exo_view`); the policy-training configs here use only > the three robot-side views, matching the experiments in the paper. The two policies expose these fields differently: - **π0.5** maps LeRobot columns to OpenPI inputs via env vars in `PI05/samples/envs/.env.habit` (`STATE_FIELDS`, `ACTION_FIELDS`, `IMAGE_KEYS`). It can auto-download a subset of `configinc/HABIT` from the Hub. - **GR00T** reads a local LeRobot v2 directory via `--dataset_path` and maps fields through `Gr00t-N1.6/examples/HABIT/habit_config.py`. Download the dataset yourself first (e.g. `git clone` / `huggingface-cli download configinc/HABIT`) and point `--dataset_path` at the resulting directory. --- ## Quick start ### π0.5 (OpenPI) — auto-downloads a 10-episode HABIT subset ```bash cd PI05 cd openpi && uv sync && cd .. # install OpenPI deps once python -m local_training.train_local samples/envs/.env.habit ``` This stages 10 episodes of the HABIT `sample` subset under `~/.cache/openpi-local/`, pulls the public π0.5 base weights, and runs a 200-step fine-tune. See [`PI05/README.md`](PI05/README.md) for scaling to the full dataset and adapting to other LeRobot v2 datasets. ### GR00T N1.6 — point it at a local HABIT checkout ```bash cd Gr00t-N1.6 # install deps per Gr00t-N1.6/README.md (uv / pip), then: # edit --dataset_path in the script to your local HABIT directory bash examples/HABIT/finetune_habit_1gpu.sh # single GPU bash examples/HABIT/finetune_habit_1node.sh # 8 GPUs, single node (torchrun) ``` Checkpoints are written to `Gr00t-N1.6/outputs/habit_finetune/` by default. --- ## Repository notes - **Vendored upstreams.** `PI05/openpi/` and `Gr00t-N1.6/` contain their respective upstream sources under their original licenses (Apache 2.0). The HABIT-specific additions are limited to `PI05/local_training/`, `PI05/samples/`, and `Gr00t-N1.6/examples/HABIT/`. - **Local-only.** Both entrypoints run on one machine (single- or multi-GPU via `torchrun`); there are no cloud/cluster orchestration dependencies in the training path. - **License.** Each vendored codebase retains its upstream license; the HABIT integration code is released under the same terms.
