--- title: X-NavDP canonical_url: "https://www.modelscope.cn/models/InternRobotics/X-NavDP" md_url: "https://www.modelscope.cn/models/InternRobotics/X-NavDP.md" repository: InternRobotics/X-NavDP last_updated: 2026-07-31 library_name: - pytorch frameworks: - pytorch downloads: 6 stars: 0 tags: - robotics - navigation - diffusion-policy - reinforcement-learning - embodied-ai - isaaclab --- # X-NavDP > X-NavDP - InternRobotics 在 ModelScope 开源的模型。X-NavDP Generalizing Navigation Diffusion Policy to Novel Behavior and Embodiments with Group Q-score Reweighted Matching Tianyu Yang 1,2   Yiming Zeng 3,2   Wenzhe Cai 2   Yuqiang Yang 2  … - **Repository**: InternRobotics/X-NavDP - **Tags**: robotics, navigation, diffusion-policy, reinforcement-learning, embodied-ai, isaaclab - **Downloads**: 6 - **Stars**: 0 - **Last updated**: 2026-07-31 Source: https://www.modelscope.cn/models/InternRobotics/X-NavDP ---

X-NavDP

Generalizing Navigation Diffusion Policy to Novel Behavior and Embodiments with Group Q-score Reweighted Matching

Tianyu Yang1,2*  Yiming Zeng3,2*  Wenzhe Cai2*  Yuqiang Yang2  Jiaqi Peng4,2  Hui Cheng3  Jiangmiao Pang2  Tai Wang2†

1Fudan University  2Shanghai AI Laboratory  3Sun Yat-sen University  4Tsinghua University

[![Project](https://img.shields.io/badge/Project-9c403d?style=flat)](https://yty-sky.github.io/x-navdp-project-page/) [![arXiv](https://img.shields.io/badge/arXiv-2607.28560-3b6291?style=flat)](https://arxiv.org/abs/2607.28560) [![Video](https://img.shields.io/badge/Video-c97937?style=flat)](https://yty-sky.github.io/x-navdp-project-page/#video) [![Benchmark](https://img.shields.io/badge/Benchmark-8A2BE2?style=flat)](../../README.md#-internvla-n1-system-1-benchmark) [![Dataset](https://img.shields.io/badge/Dataset-548B54?style=flat)](https://huggingface.co/datasets/InternRobotics/Scene-N1/tree/main/n1_eval_scenes) [![GitHub star chart](https://img.shields.io/github/stars/InternRobotics/NavDP?style=square)](https://github.com/InternRobotics/NavDP)

Installation | Assets | Training | Evaluation | Results | Citation

## Introduction X-NavDP post-trains a pretrained RGBD camera-based navigation diffusion policy via online reinforcement learning across heterogeneous embodiments. Beyond improving general navigation and obstacle avoidance performance, it gains new capabilities for backing out of traps, long-obstacle detours, and embodiment-aware behavior adaptation. Our contributions to navigation diffusion policy are threefold: - ⚡ **Data-efficient RL post-training.** X-NavDP enhances pretrained diffusion policies through efficient large-scale, multi-scene online post-training for stronger general navigation ability. - ⚒ **Structured exploration and stable training.** Goal-agnostic diffusion trajectories and **Group Q-score Reweighted Matching** enable structured exploration, improve training stability, and handle hard cases. - 🤝 **Cross-robot generalization and temporal consistency.** Lightweight embodiment modulation and RTC guidance improve cross-robot generalization and temporal consistency, leading to superior post-trained navigation performance.

## Installation Clone NavDP and enter the self-contained X-NavDP baseline: ```bash git clone https://github.com/InternRobotics/NavDP.git cd NavDP/baselines/x-navdp ``` Create a Python environment and install the dependencies: ```bash conda create -n navrl python=3.11 conda activate navrl pip install -r requirements.txt ``` Install [Isaac Sim 5.0.0](https://docs.isaacsim.omniverse.nvidia.com/5.0.0/installation/index.html), [Isaac Lab 0.46.2](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/index.html), and `isaaclab-rl==0.4.0` following their official instructions. Then install acados following the [acados installation guide](https://docs.acados.org/installation/index.html) and [Python interface guide](https://docs.acados.org/python_interface/index.html). The acados setup is lightweight and mainly requires a CMake build plus the Python interface. Before running training or evaluation, expose the acados libraries and accept the Omniverse EULA: ```bash export ACADOS_SOURCE_DIR=/path/to/acados export LD_LIBRARY_PATH="${ACADOS_SOURCE_DIR}/lib:${LD_LIBRARY_PATH:-}" export OMNI_KIT_ACCEPT_EULA=YES export OMNI_KIT_ALLOW_ROOT=1 ``` ## Assets Large assets, datasets, robot USDs, low-level controller checkpoints, and pretrained policy checkpoints are not included in this repository. You need to download these assets manually before running training or evaluation. The **GRScenes100 / N1 evaluation scenes** are available from [InternRobotics/Scene-N1](https://huggingface.co/datasets/InternRobotics/Scene-N1/tree/main/n1_eval_scenes). This dataset contains sky textures, materials, cluttered easy/hard scenes, InternScenes home scenes, and InternScenes commercial scenes. The **X-NavDP assets** are available from [InternRobotics/X-NavDP](https://huggingface.co/InternRobotics/X-NavDP/tree/main). This repository provides `navigation_metadata`, robot assets, low-level controller checkpoints, `scene_split.json`, the NavDP pretrained checkpoint, and the X-NavDP post-trained checkpoint. After downloading, place or symlink the scene data, metadata, robot assets, and checkpoints in the following layout. The scene root can also be kept elsewhere and passed with `SCENE_DIR`.
**Scene data and navigation metadata** ```text x-navdp/ +-- data/scenes/ +-- scene_split.json +-- SkyTexture/ +-- Materials/ +-- cluttered_easy/ +-- cluttered_hard/ +-- internscenes_commercial/ | +-- models/ | +-- Materials/ | +-- scenes_commercial/ +-- internscenes_home/ | +-- models/ | +-- Materials/ | +-- scenes_home/ +-- navigation_metadata/ +-- internscenes_commercial/ +-- internscenes_home/ +-- cluttered_easy/ +-- cluttered_hard/ ``` **Code, robot assets, and checkpoints** ```text x-navdp/ +-- train.py +-- config/ +-- scripts/ +-- src/ | +-- environment/ | | +-- controllers/checkpoints/ | | +-- humanoid_g1/policy.pt | | +-- quadruped_go2/policy.pt | +-- x_navdp/ | +-- training/ | +-- utils/ +-- eval/ +-- data/robots/ | +-- dingo.usd | +-- unitreeg1.usd +-- pretrain_model/ | +-- navdp_pretrained.ckpt +-- checkpoints/ +-- x-navdp_posttrain.ckpt ```
Training and evaluation expect `SCENE_DIR` to point to the scene root. The training config initializes from `pretrain_model/navdp_pretrained.ckpt`; evaluation examples use the released post-trained checkpoint under `checkpoints/`. Dingo and G1 load robot USDs from `data/robots/`, while Unitree Go2 uses the Isaac Lab asset path by default. ## Training The released [post-trained model](https://huggingface.co/InternRobotics/X-NavDP/blob/main/x-navdp_posttrain.ckpt) was trained for 24,000 steps on 72 scenes, including 16 clutter scenes and 56 home/commercial scenes. Single-node 8-GPU distributed training can be launched with: ```bash export SCENE_DIR=/path/to/NavDP/baselines/x-navdp/data/scenes export NPROC_PER_NODE=8 bash scripts/run_ddp_train.sh \ --config_file config/x-navdp_config.yaml ``` For a smaller debug run, reduce the number of processes and environments: ```bash export NPROC_PER_NODE=1 bash scripts/run_ddp_train.sh \ --num_envs 1 \ --embodiments dingo \ --max_steps 100 ``` Training writes periodic evaluation records to the directory passed through `--txt_dir`. Aggregate these logs into global, per-embodiment, and per-scene success-rate CSV files and plots with: ```bash python scripts/aggregate_success.py ./txt/x-navdp ``` By default, outputs are written to `./result//`; for the command above, the output directory is `./result/x-navdp/`. Each log row has the form `rank,step,episode,success,trainer_success_rate`. Home/commercial and clutter results are reported separately. Use `--output-dir` to override the result directory, or `--no-plots` to generate CSV files only; run `python scripts/aggregate_success.py --help` for EMA and milestone options. ## Evaluation Evaluation uses a policy server and an Isaac Lab client. Start the policy server: ```bash bash eval/scripts/start_policy_server.sh \ --checkpoint checkpoints/x-navdp_posttrain.ckpt \ --embodiment quadruped ``` Run point-goal evaluation: ```bash bash eval/scripts/run_evaluation.sh \ --config_file eval/config/eval_pointgoal/quadruped_internscene_commercial.yaml ``` By default, evaluation runs all start-goal samples in the selected scene. Use `--num_episodes` to evaluate a subset and `--max_steps` to cap the total simulation steps for smoke tests or debugging. Evaluation configs are provided under `eval/config/eval_pointgoal/`, and outputs are written under `outputs/evaluation/_/`. Each scene directory contains a `metric.csv` file whose first two columns are success and SPL. To print per-USD scene SR and SPL, run: ```bash python eval/scripts/stat_eval_metrics.py outputs/evaluation/quadruped_commercial ``` ## Results On the project benchmark, X-NavDP improves the overall simulation success rate from 61.20% to 84.28% and improves real-world hard-case success rate from 10% to 65%. See the [project results](https://yty-sky.github.io/x-navdp-project-page/#results) for simulation and real-world comparisons across wheeled Dingo, quadruped Unitree Go2, and humanoid Unitree G1 embodiments. ## Citation ```bibtex @misc{yang2026xnavdp, title = {X-NavDP: Generalizing Navigation Diffusion Policy to Novel Behavior and Embodiments with Group Q-score Reweighted Matching}, author = {Tianyu Yang and Yiming Zeng and Wenzhe Cai and Yuqiang Yang and Jiaqi Peng and Hui Cheng and Jiangmiao Pang and Tai Wang}, year = {2026} } ``` ## License Original X-NavDP code is released under the [MIT License](LICENSE). Vendored and external dependencies retain their upstream terms; see [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md). ## Acknowledgement This project builds on Isaac Sim, Isaac Lab, acados, and NavDP. X-NavDP is distributed as a self-contained baseline in the [InternRobotics/NavDP](https://github.com/InternRobotics/NavDP) repository.