Spaces:
Running on Zero
Running on Zero
| title: 3D HAMSTER — 3D Trajectory Prediction | |
| emoji: 🐹 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 6.15.1 | |
| app_file: app.py | |
| short_description: Predict metric 3D robot trajectories from RGB-D + language | |
| python_version: "3.12" | |
| startup_duration_timeout: 1h | |
| pinned: false | |
| license: apache-2.0 | |
| models: | |
| - DAVIAN-Robotics/3D_HAMSTER | |
| # 🐹 3D HAMSTER — 3D Trajectory Prediction | |
| Interactive demo for **3D HAMSTER**, a depth-aware Vision-Language-Action planner | |
| that predicts **metrically grounded 3D end-effector trajectories** directly from a | |
| single RGB-D observation and a language instruction. | |
| - **Backbone**: Qwen3-VL-8B + a frozen LingBot-Depth (DINOv2 ViT-L/14) geometry encoder. | |
| - **Inputs**: RGB image + metric depth map (`.npy`, meters, aligned to RGB) + instruction. | |
| - **Outputs**: `[u, v, depth]` waypoints + gripper actions, visualized as a 2D overlay | |
| and an interactive 3D scene + trajectory. | |
| - **Modes**: 3D Trajectory (default), 2D Trajectory, 3D/2D Pointing, 2D Bounding Box, General VQA. | |
| > ⚠️ Depth must be **metric (meters)** and aligned to the RGB frame. | |
| Links: [Paper](https://huggingface.co/papers/2606.31329) · | |
| [Model](https://huggingface.co/DAVIAN-Robotics/3D_HAMSTER) · | |
| [Code](https://github.com/DAVIAN-Robotics/3D_HAMSTER) · | |
| [Project page](https://davian-robotics.github.io/3D_HAMSTER/) | |