Human Demonstrations and RL Data Yield Different Chunking Effectiveness: Activity Sparsity as a Key Indicator

Action chunking has emerged as a promising technique for robotic learning, yet it remains unclear when an action-chunking policy can be more effective. Our extensive empirical studies reveal that action chunking policies trained on human demonstrations perform well on standard benchmarks with moderate chunking sizes, whereas those trained on reinforcement learning (RL)-generated data often degrade as the chunk size increases. We investigate this intriguing human-RL gap in chunking effectiveness through the lens of activity sparsity. Our findings show that human demo datasets generally exhibit higher activity sparsity than RL-generated datasets across tasks. Building on this insight, we introduce sparsity regularization into the RL data-generation process and show that the resulting datasets can improve the effectiveness of action chunking policies.