arXiv (All)
open access
Continuous-Time Motion Planning and Steering with Global Convergence
Motion planning is central to robotics, and it is increasingly used to correct the motions proposed by learned planners and policies. In both uses, constraints must hold at every instant of the motion, not only at sampled times. To our knowledge, no algorithm provably converges to a globally optimal
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
- Reinforced Iterative Learning Control for elastic joint arms: Guaranteed cross-trajectory generalization under model uncertainties#1412298
- ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation#629735
- ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation#627873
- Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors#1070646
- Learning Waypoints and Passage Events for Decentralized Multi-Quadrotor Trajectory Planning#1389970
Record · ID 1423399
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