arXiv (All)
open access
Model Predictive Control of Tensegrity Robots via Contact-Aware Graph Neural Dynamics Model
Tensegrity robots offer lightweight, compliant mobility over challenging terrain but remain difficult to model and control due to complex contact-rich dynamics and partial observability. This work presents a model predictive path integral (MPPI) controller for a three-bar tensegrity robot driven by
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
- Learning-enabled Acceleration of Scenario-based Model Predictive Control#366257
- 2AM: Grounding Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation#998845
- SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control#662503
- SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control#661453
- SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control#480097
Record · ID 986577
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