Load-Aware Quadruped Navigation with Online Contact-based Geometry Estimation
To fill this knowledge gap, we propose a two-phase load-aware navigation learning framework.
ProofPaper ↗
Key points
- Navigating cluttered environments while carrying objects remains a challenging problem for quadruped robots.
- Compared with works that focused on load-aware locomotion adaptation to maintain gait stability, existing autonomous navigation systems generally ignore the influence of carried objects' geometries and use fixed footprint inflation, leading to overly conservative navigation.
- In Phase 1, we train LOADNav, a load-aware navigation policy via reinforcement learning, enabling the robot to adapt its trajectory and clearance behavior according to the geometric size of the carried object.
- The results show that our navigation policy exhibits clear load-aware behaviors, and the online geometry predictor estimates object geometry from occasional contact events, refining its estimate and adapting the navigation behavior accordingly.
Sources (1)
- [1]Load-Aware Quadruped Navigation with Online Contact-based Geometry EstimationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 05:32 AM
To fill this knowledge gap, we propose a two-phase load-aware navigation learning framework.
Navigating cluttered environments while carrying objects remains a challenging problem for quadruped robots.
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