--- license: mit tags: - robotics - bfn - bayesian-flow-networks - diffusion-policy - pusht - xarm - hybrid-action --- # PushT-xarm Real-Robot Policies (BFN-Hybrid vs DDPM-OneHot) Real-robot push-T policies trained on [borueihuang/pusht_xarm_merged](https://huggingface.co/datasets/borueihuang/pusht_xarm_merged). Both policies were trained on 144 episodes / 7835 frames at 30 Hz, top camera only, 200 epochs. ## Action space - **Discrete:** 8 push directions (`action.direction` in {0..7}) - **Continuous:** push distance (`action.distance` in `[0, 50]`) ## Observation space - `camera_0`: top-view RGB image, 3x224x224, two-step history (`n_obs_steps=2`) ## Policies | File | Method | Action treatment | Inference steps | |------|--------|------------------|-----------------| | `bfn/latest.ckpt` | **BFN-Hybrid** (categorical + continuous Bayesian flow) | true hybrid | 20 | | `ddpm/latest.ckpt` | **DDPM** | one-hot continuous (9D) | 100 | ## Quick start (BFN) ```bash pip install -r requirements.txt python inference.py --ckpt bfn/latest.ckpt --config bfn/policy_config.yaml ``` Programmatic use: ```python from inference import load_bfn_policy, infer_step policy = load_bfn_policy("bfn/latest.ckpt", "bfn/policy_config.yaml", "cuda") actions = infer_step(policy, cam0_now, cam0_prev, "cuda") # actions: List[{"direction": int 0..7, "distance": float 0..50}], len = n_action_steps (8) ``` ## DDPM checkpoint The DDPM policy uses `diffusion_policy.policy.diffusion_unet_hybrid_image_policy.DiffusionUnetHybridImagePolicy` from the `diffusion-policy` library. Action is a 9D continuous vector: `[one_hot(8), distance]`. At inference time, take `argmax` of the first 8 dims for the direction, and the 9th dim for distance. ## Files ``` bfn/ latest.ckpt policy_config.yaml ddpm/ latest.ckpt policy_config.yaml bfn_hybrid_image_policy.py # standalone BFN policy class policies/base.py # BasePolicy abstract class networks/base.py # BFNetwork wrapper inference.py # example loader + inference requirements.txt ```