arXiv (All)
open access
Predictive Safety Curricula for Robust Legged Locomotion
Rare but consequential failures can persist in learned locomotion policies for legged robots even when average task performance is high, in part because standard curricula primarily adapt task difficulty rather than the distribution of safety-critical experience. We introduce Predictive Safety Curri
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation#1104441
- CRAX: Fast Safe Reinforcement Learning Benchmarking#1118615
- From Structural Dynamics to Robot Safety Runtime Assurance, Hard Constraints, and Persistent Correction in Embodied AI#695093
- Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning#216821
- Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning#1034723
Record · ID 1132271
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive