arXiv (All)
open access
SwingRL: Adaptive Observation Reinforcement Learning with World-Model Prediction for Cable-Suspended Hoisting Control
Cable-suspended hoisting is widely used to move heavy or bulky payloads that cannot be handled conveniently by rigid pick-and-place systems, for example in crane-assisted construction. Robotic hoisting using flexible cables is challenging because payload motion is underactuated, external disturbance
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
- Compliantly-Driven Robots with Application to Exoskeletons : Mechanical Design, Interaction Control, and Interactive Learning / by Xiang Li, Haoyong Yu#1097104
- ReCo: Response-Consistent Locomotion with Policy-Aware MPC for Legged Manipulation#1156883
- Learning-Based Progressive Barrier Control for Robot Manipulators with Initial Errors Outside Prescribed Tracking Bounds#1144710
- DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions#1118577
- Fault-Tolerant, Rigidity-Preserving Control of Inflatable Truss Robots#307660
Record · ID 1117765
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