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

Problem
The paper addresses the limitations of existing motion planning algorithms, particularly the Sample-efficient Cross-Entropy Method (iCEM), in complex robotic manipulation tasks such as stacking, sliding, and shelf placement. The authors highlight that while iCEM has shown promise in simpler control tasks, its performance diminishes in more intricate scenarios. This work is a preprint and has not undergone peer review, indicating that the findings should be interpreted with caution.

Method
The authors propose the iCEM+TL framework, which integrates Transfer Learning (TL) into the iCEM architecture. This framework allows for the transfer of key parameters from simpler upstream tasks to enhance the performance of more complex downstream tasks. Additionally, the authors implement Reward Redesign (RR) through task decomposition, specifically tailored for stacking and shelf placement tasks. The training process involves simulating various manipulation tasks, where the iCEM+TL framework is evaluated against standard benchmarks. The authors do not disclose specific training compute resources but emphasize the efficiency of their approach in leveraging prior knowledge.

Results
The iCEM+TL framework demonstrates a significant improvement in success rates, achieving enhancements of up to 23% compared to the baseline performance of the original iCEM on complex manipulation tasks. The framework’s efficacy is further validated through experiments on a real Franka Emika robot, where it successfully executes stacking tasks, showcasing its practical applicability in real-world scenarios. The results indicate that the integration of TL and RR substantially boosts the performance of robotic manipulation systems.

Limitations
The authors acknowledge that the framework’s performance may still be constrained by the complexity of the tasks and the quality of the upstream tasks from which parameters are transferred. They also note that the current implementation focuses primarily on stacking and shelf placement, suggesting that further exploration is needed for a broader range of manipulation tasks. Additionally, the reliance on simulation for initial validation may not fully capture the challenges encountered in diverse real-world environments.

Why it matters
This work has significant implications for the field of robotic manipulation, particularly in enhancing the efficiency and effectiveness of motion planning algorithms. By demonstrating the potential of transfer learning and reward redesign in improving task performance, the iCEM+TL framework paves the way for more sophisticated robotic systems capable of handling complex manipulation tasks with reduced training times. The findings contribute to the ongoing discourse on optimizing robotic learning strategies, as discussed in related literature, and are available on arXiv.