Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning

Researchers propose iCEM+TL, a framework combining the Cross-Entropy Method with transfer learning to improve robotic manipulation planning efficiency. The approach achieves up to 23% success rate improvements in complex tasks like stacking and shelf placement, with validation demonstrated on a real Franka Emika robot.

This research addresses a fundamental challenge in robotics: reducing sample complexity and training time for motion planning models as systems become more sophisticated. The proposed iCEM+TL framework represents an incremental but meaningful advancement in how evolutionary algorithms can leverage transfer learning to optimize real-time robotic control.

The work builds on recent progress in sample-efficient planning methods, where the Cross-Entropy Method has shown promise for low-level control tasks. The key innovation involves explicitly transferring learned parameters from simpler source tasks to more complex target tasks, combined with reward redesign through task decomposition. This dual approach addresses both the parameter optimization problem and the task specification challenge that limits iCEM performance in complex manipulation scenarios.

For the robotics and automation industry, this framework offers practical benefits: reduced computational overhead, faster training cycles, and improved performance on dexterous manipulation tasks. Companies developing robotic systems for manufacturing, logistics, or service applications could realize shorter deployment times and better real-world performance. The successful real-robot validation on the Franka Emika platform suggests the method translates from simulation to practice, reducing the domain gap problem that often plagues simulation-trained policies.

The 23% success rate improvement is particularly significant for shelf placement and stacking tasks, which are critical for warehouse automation and object manipulation applications. As robotics becomes increasingly commoditized and accessible to smaller organizations, methods that reduce training complexity and improve performance efficiency could accelerate adoption. Future work likely involves scaling to more complex manipulation tasks and exploring how transfer learning hierarchies could enable even greater sample efficiency gains.

• →iCEM+TL framework combines evolutionary algorithms with transfer learning to improve robotic motion planning by up to 23%
• →Reward redesign through task decomposition optimizes performance for complex manipulation tasks like stacking and shelf placement
• →Successfully validated on real Franka Emika robot, demonstrating practical feasibility beyond simulation
• →Framework reduces sample complexity and training time for increasingly sophisticated robotic systems
• →Transfer of iCEM parameters from simpler to complex tasks enables efficient knowledge reuse and guides downstream task learning