Skill: the0xka1/habitat-gs-navigator (version 0.1.1)

--- name: habitat-gs-navigator description: "Navigate and interact with photo-realistic 3DGS environments via the Habitat-GS Bridge. Use when: user asks to explore a 3D scene, perform embodied navigation, do Embodied QA tasks, run navigation episodes in Habitat-GS, or interact with the Habitat-GS simulator. Triggers on: 'navigate', 'habitat', '3DGS scene', 'embodied', 'load scene', 'explore room', 'EQA'. Requires the Habitat-GS Bridge server (pip install habitat-gs-bridge) running at localhost:8890." --- # Habitat-GS Navigator Control an embodied agent inside photo-realistic 3D Gaussian Splatting environments through the Habitat-GS Bridge. ## Installation ```bash git clone https://github.com/The0xKa1/habitat-gs-bridge.git cd habitat-gs-bridge pip install -e . ``` This provides two commands: - `hab-cli` — CLI for controlling the simulator (used by this skill) - `habitat-gs-bridge` — starts the bridge server For full API details, read [references/api-reference.md](references/api-reference.md). For setup instructions, read [references/setup.md](references/setup.md). ## Quick Workflow ```bash # 1. Start the bridge server (in a separate terminal) habitat-gs-bridge # 2. Verify it's running hab-cli status # 3. Load scene (by scene-id + dataset, or by direct path) hab-cli load_scene --scene-id gs_scene --dataset /path/to/config.json hab-cli load_scene --scene /path/to/scene.gs.ply # 4. Reset episode with start/goal hab-cli reset --start "5.18,-3.57,-2.86" --goal "-3.62,-3.61,3.18" # 5. Navigate: observe → decide → act → repeat hab-cli step move_forward hab-cli step turn_left hab-cli step turn_right hab-cli step stop # when goal reached # 6. Utilities hab-cli observe # current observation without stepping hab-cli path --goal "x,y,z" # shortest-path info hab-cli random_point # sample navigable point ``` ## Navigation Loop 1. **Observe**: read `agent_state.position`, `distance_to_goal`, `collided` 2. **Decide**: use the philosophical-three-questions skill (Goal/State/Future tree) 3. **Act**: pick one of `move_forward`, `turn_left`, `turn_right`, `stop` 4. **Check**: verify distance decreased; if collided, turn to find open path 5. **Repeat** until `done` is true or `distance_to_goal` < `goal_radius` ## Decision Heuristics - `collided` after `move_forward` → turn (try left, then right) to find open path - `distance_to_goal` decreasing → keep current heading - `distance_to_goal` stagnant/increasing → change direction, use `hab-cli path` to check geodesic distance - `distance_to_goal` < 0.5m → call `stop` - Near `max_steps` → consider `stop` if reasonably close ## Configuration The bridge server URL defaults to `http://127.0.0.1:8890`. Override with: - `--url` flag: `hab-cli --url http://host:port status` - Environment variable: `export HABITAT_GS_BRIDGE_URL=http://host:port` ## Experience Logging After each episode, record to `~/.openclaw/workspace/memory/YYYY-MM-DD.md`: ```markdown ## [NAV] Episode <id> in <scene> - Result: success/fail (N steps, optimal: M steps) - Key decisions: <turning points> - Lesson: <what to do differently> ``` After 5+ episodes, review memory and extract recurring patterns into new skills or update this skill's heuristics.


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