Core Concepts
Knowledge transfer and curriculum learning strategies enhance robotic manipulation efficiency and effectiveness.
Abstract
The study explores learning-based tri-finger robotic arm manipulation tasks using reinforcement learning. It delves into fine-tuning and curriculum learning strategies within the soft actor-critic architecture. The impact of these strategies on learning efficiency, generalization, and performance is thoroughly analyzed. The study compares whole network transfer and policy-only transfer methods, as well as the effectiveness of cross-task fine-tuning. Additionally, the design parameters and timing of curriculum learning stages are investigated. Results and discussions highlight the key findings and implications for complex manipulation tasks.
Stats
The agent was pre-trained on grasping a block before fine-tuning for pushing, reducing pre-training time.
Curriculum learning in a 2-stage approach accelerated learning efficiency in pushing blocks of varied sizes.
Policy-only transfer showed better performance in context-unaware scenarios compared to whole network transfer.
The 3-stage curriculum outperformed the 2-stage curriculum in context-aware scenarios.
Cross-task fine-tuning achieved comparable results to within-task fine-tuning with reduced pre-training time.
Quotes
"The whole network transfer demonstrated superiority over policy-only transfer in scenarios requiring context-awareness."
"Cross-task fine-tuning proved to be highly efficient, achieving comparable training outcomes to within-task fine-tuning while significantly reducing the need for pre-training time."
"The 2-stage curriculum, with the sub-task transition occurring at 1200 timesteps, emerged as the optimal solution for pushing blocks of varied sizes."