arXiv:2603.14392v2 Announce Type: replace Abstract: Trajectory world models play a crucial role in robotic dynamics learning, planning, and control. While recent works have explored trajectory world models for diverse robotic systems, they struggle to scale to a large number of distinct system dynamics and overlook domain knowledge of physical structures. To address these limitations, we introduce WestWorld, a knoWledge-Encoded Scalable Trajectory World model for diverse robotic systems. To tackle the scalability challenge, we propose a novel system-aware Mixture-of-Experts (Sys-MoE) that dyna
The proliferation of diverse robotic systems necessitates more scalable and intelligent control models, driving innovation in AI approaches that incorporate domain knowledge.
This development in trajectory world models could significantly accelerate the deployment and adaptability of robotics across various industries, impacting automation capabilities.
The ability to scale world models across many distinct robotic systems by incorporating physical structure knowledge changes the paradigm for robotic dynamics learning and control.
- · Robotics manufacturers
- · AI hardware developers
- · Logistics and manufacturing sectors
- · Advanced automation providers
- · Companies with proprietary, non-scalable robotics platforms
- · Manual labor in repetitive tasks
Increased efficiency and flexibility in robotic deployment for complex tasks.
Broader adoption of robotics in sectors where customized solutions were previously too expensive or difficult to implement.
Potential for new classes of autonomous systems that can rapidly adapt to novel physical environments and tasks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG