PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning
This addresses data inefficiency in robot imitation learning, offering a domain-specific improvement for multi-stage manipulation tasks.
The paper tackles the problem of high sample complexity in imitation learning for long-horizon robot manipulation tasks by introducing PRIME, a framework that decomposes tasks into behavior primitives, resulting in 10-34% higher success rates in simulation and 20-48% on hardware compared to baselines.
Imitation learning has shown great potential for enabling robots to acquire complex manipulation behaviors. However, these algorithms suffer from high sample complexity in long-horizon tasks, where compounding errors accumulate over the task horizons. We present PRIME (PRimitive-based IMitation with data Efficiency), a behavior primitive-based framework designed for improving the data efficiency of imitation learning. PRIME scaffolds robot tasks by decomposing task demonstrations into primitive sequences, followed by learning a high-level control policy to sequence primitives through imitation learning. Our experiments demonstrate that PRIME achieves a significant performance improvement in multi-stage manipulation tasks, with 10-34% higher success rates in simulation over state-of-the-art baselines and 20-48% on physical hardware.