Habitat-GS-Navigator

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.

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. For setup instructions, read 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.