HORIZON: Recoverability-Governed Curriculum for Physical-Domain Scaling
HORIZON uses recoverability-governed curriculum to expand physical domains, enhancing quadruped robots' zero-shot transfer abilities.
Key Findings
Methodology
HORIZON expands physical domains using checkpoint rollback and boundary refinement, only within the recoverable boundary of the current policy. This method transforms fixed randomization into a continual process of physical-domain growth.
Key Results
- Experiments show direct domain widening is uneven across physical axes and often unlearnable without staged ordering.
- Domain composition is non-monotonic, adding more domains can dilute recoverable samples and reduce robustness.
- Offline distillation of isolated experts cannot substitute for the joint interaction generated by on-policy curriculum.
Significance
This study provides a framework for embodied control's physical-domain generalization, emphasizing recoverability as the organizing principle for policy expansion. It impacts the field of robot learning by addressing long-standing issues in physical-domain scaling.
Technical Contribution
HORIZON introduces a new curriculum learning method that governs physical-domain expansion through recoverability, avoiding unrecoverable failures seen in traditional randomization methods.
Novelty
This is the first to use recoverability as a core constraint for physical-domain expansion, differing from previous randomization methods by offering a new curriculum learning perspective.
Limitations
- Currently validated only on quadruped robots, not yet extended to other robot morphologies.
- Physical domain grouping is manually specified, potentially limiting automated expansion potential.
Future Work
Future work could explore applying this method to other robot morphologies or integrating visual domains for broader generalization studies.
AI Executive Summary
Existing robot policy expansion methods often rely on broad randomization, which can lead to unrecoverable failures. HORIZON uses recoverability-governed curriculum to expand physical domains, only within the recoverable boundary of the current policy. Experiments show direct domain widening is uneven across physical axes and often unlearnable without staged ordering. Domain composition is non-monotonic, adding more domains can dilute recoverable samples and reduce robustness. Offline distillation of isolated experts cannot substitute for the joint interaction generated by on-policy curriculum. This study provides a framework for embodied control's physical-domain generalization, emphasizing recoverability as the organizing principle for policy expansion. It impacts the field of robot learning by addressing long-standing issues in physical-domain scaling.
Deep Analysis
Background
Robot learning domain expansion typically relies on randomization methods, which can lead to unrecoverable failures. Existing methods struggle to maintain policy recoverability during physical-domain expansion.
Core Problem
How to maintain policy recoverability while expanding physical domains is a core problem. This is crucial for achieving broader robot generalization capabilities.
Innovation
HORIZON uses recoverability-governed curriculum to expand physical domains, avoiding unrecoverable failures seen in traditional randomization methods. This method transforms fixed randomization into a continual process of physical-domain growth.
Methodology
- �� Use checkpoint rollback to avoid unrecoverable expansions
- �� Control each expansion step through boundary refinement
- �� Transform physical-domain expansion into a continual growth process
Experiments
Experiments use quadruped robots as benchmarks to test expansion effects across different physical axes. Results show recoverability is key to successful expansion.
Results
Experiments show direct domain widening is uneven across physical axes and often unlearnable without staged ordering. Domain composition is non-monotonic, adding more domains can dilute recoverable samples and reduce robustness.
Applications
This method can enhance robots' adaptability in unknown environments, especially in complex physical domains.
Limitations & Outlook
Currently validated only on quadruped robots, not yet extended to other robot morphologies. Physical domain grouping is manually specified, potentially limiting automated expansion potential.
Plain Language Accessible to non-experts
Imagine you're in a kitchen trying to learn how to cook a new dish. Each time you try a new ingredient, you make sure it won't ruin the whole dish. HORIZON is like a smart chef who knows when to try new ingredients and ensures each attempt makes the dish tastier, not ruin it.
ELI14 Explained like you're 14
Imagine you're playing a game where each time you try a new level, the game ensures you won't fail too badly. HORIZON is like a level designer in the game, making sure each new level teaches you something without making you lose all the time. Isn't that cool?
Glossary
Recoverability
The ability of a policy to recover from failures during physical-domain expansion.
Used to control the boundaries of physical-domain expansion.
Curriculum Learning
A learning method that improves learning outcomes by gradually increasing task difficulty.
Used to organize the process of physical-domain expansion.
Physical-Domain Scaling
Expanding the range of physical environments a robot policy can adapt to.
The core goal of HORIZON.
Checkpoint Rollback
Returning to a previously successful state when a policy fails.
Used to avoid unrecoverable physical-domain expansions.
Boundary Refinement
Adjusting expansion boundaries to ensure policy recoverability.
Used to control each step of physical-domain expansion.
Open Questions Unanswered questions from this research
- 1 How to automate physical-domain grouping and expansion remains an open question.
- 2 Further research is needed to validate on other robot morphologies.
Applications
Immediate Applications
Robot Adaptability Training
Enhance robots' adaptability in complex environments by expanding physical domains through recoverability-governed curriculum.
Long-term Vision
Universal Robot Control
Achieve universal robot control in various environments through continual physical-domain expansion.
Abstract
Scaling robust robot policies requires more than broader randomization, because physical-domain experience must remain organized and learnable throughout training. We study when a policy can benefit from harder physics and identify recoverability as a central constraint in on-policy physical-domain scaling. In on-policy training, new dynamics are useful only insofar as they remain close enough to the current policy to generate corrective on-policy data, rather than collapsing rollouts into unrecoverable failures. Using quadruped locomotion as a physically demanding benchmark for embodied generalization, we introduce HORIZON, a checkpointed frontier curriculum that expands physical domains only within the current policy's recoverable boundary. HORIZON uses rollback and boundary refinement to govern each expansion step, turning fixed randomization into a continual process of physical-domain growth. Experiments reveal three regularities of physical-domain expansion. First, direct domain widening is uneven across physical axes and often unlearnable without staged ordering. Second, domain composition is non-monotonic, and adding more domains beyond a compact core can dilute recoverable joint samples and reduce overall robustness. Third, offline distillation of isolated experts cannot substitute for the joint interaction generated by on-policy curriculum. Together, these results frame physical-domain generalization as a continual growth problem for embodied control, with recoverability as the organizing principle for on-policy expansion.