Persistent Profile-Guided Reset-Free Recovery Improves Final Gait Selection after Irreversible Leg Damage
We introduce persistent profile-guided reset-free recovery (PGRR), which predicts multiple outcomes over repeated gait execution and uses conservative uncertainty-aware estimates to guide short online probes without resetting the robot.
ProofPaper ↗
Key points
- Damage-recovery algorithms often rank gaits from short trials, yet a gait that performs well briefly may not remain effective during continued execution.
- Across 108 matched MuJoCo tasks spanning leg amputation, distal fracture, and joint lock, PGRR improves final quality by 0.106 and slot-averaged recovery quality (SRQ) by 0.044 relative to an IT&E-inspired Gaussian-process baseline, while reducing low-quality failures by 27.8 percentage points.
- Development ablations identify conservative uncertainty handling as the clearest supported contributor to the observed gains.
- These results show that PGRR can identify stronger post-damage gaits under continuous physical state and that final-gait quality and recovery-summary performance provide complementary evidence for evaluating reset-free recovery.
Sources (1)
- [1]Persistent Profile-Guided Reset-Free Recovery Improves Final Gait Selection after Irreversible Leg DamagearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 07:26 PM
We introduce persistent profile-guided reset-free recovery (PGRR), which predicts multiple outcomes over repeated gait execution and uses conservative uncertainty-aware estimates to guide short online probes without resetting the robot.
Damage-recovery algorithms often rank gaits from short trials, yet a gait that performs well briefly may not remain effective during continued execution.
Extractive summary: sentences quoted from the sources.