- 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
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
# 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 pointNavigation Loop
- Observe: read
agent_state.position,distance_to_goal,collided - Decide: use the philosophical-three-questions skill (Goal/State/Future tree)
- Act: pick one of
move_forward,turn_left,turn_right,stop - Check: verify distance decreased; if collided, turn to find open path
- Repeat until
doneis true ordistance_to_goal<goal_radius
Decision Heuristics
collidedaftermove_forward→ turn (try left, then right) to find open pathdistance_to_goaldecreasing → keep current headingdistance_to_goalstagnant/increasing → change direction, usehab-cli pathto check geodesic distancedistance_to_goal< 0.5m → callstop- Near
max_steps→ considerstopif reasonably close
Configuration
The bridge server URL defaults to http://127.0.0.1:8890. Override with:
--urlflag: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:
## [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.