Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning
arXiv:2606.06041v1 Announce Type: cross Abstract: As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorithms such as the Sample-efficient Cross-Entropy Method (iCEM) have recently demonstrated promising potential for low-level real-time planning by leveraging efficient knowledge reuse strategies to improve performance. Although effective in many control tasks, iCEM's performance can be constrained in more complex scenarios, particularly those requiring stacking, sliding,
This research addresses the growing challenge of complex motion planning in advanced robotic systems, highlighting the current bottlenecks in training efficiency, which is a critical area of focus as robotics mature.
Improved sample-efficient motion planning through zero-shot transfer learning directly accelerates the development and deployment of sophisticated robotic manipulation, reducing development costs and increasing adaptability for new tasks.
The ability to transfer learning efficiently between robotic tasks without extensive retraining could significantly reduce the time and computational resources required to deploy robots in new, complex environments.
- · Robotics companies
- · Automation sector
- · Manufacturing
- · Logistics
- · Traditional robotics development with long training cycles
Robotic systems will become more adaptable and quicker to deploy in diverse and complex environments like factories and warehouses.
Reduced development costs and faster deployment cycles could accelerate the adoption of advanced robotics across various industries, creating new market opportunities.
The widespread deployment of highly adaptable robots could lead to significant shifts in labor markets, requiring new forms of human-robot collaboration.
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Read at arXiv cs.AI