arXiv (All)
open access
MotionForge: A Data Generation Pipeline and Large-Scale Benchmark for Long-Horizon Manipulation of Dynamic Objects with Domain Shifts
Recent advances in learning-based robot policies have demonstrated promising progress, yet they are predom- inantly evaluated in static or quasi-static environments. In dynamic manipulation, objects and scenes continuously evolve while the robot perceives, reasons, and acts. However, recent dynamic
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
- MoWAM: Explicit Future Motion Prediction for Efficient World Action Models#1034507
- Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Control#1119091
- LIBERO-MAX: Do Robot Policies Adapt When the World Changes?#1131719
- Accelerating Visual Policy Learning with Sampling-Based Model Predictive Control#1034388
- Accelerating Visual Policy Learning with Sampling-Based Model Predictive Control#978430
Record · ID 1061324
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