PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning

Abstract: 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.

• The paper introduces a framework that integrates behavior primitives to reduce the sample complexity of imitation learning for robotic manipulation.
• It employs a two-step process using an inverse dynamics model for unsupervised primitive segmentation and high-level policy training via behavioral cloning.
• Evaluations demonstrate enhanced success rates and robust recovery in both simulated environments and real-world robotic tasks.

An Expert Overview of "PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning"

The paper "PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning," introduces a novel framework aimed at enhancing the data efficiency of imitation learning used for robotic manipulation tasks. The authors present PRIME, a framework that leverages behavior primitives to decompose complex tasks into manageable sequences, thereby reducing the challenges associated with long-horizon tasks in imitation learning.

Core Contribution

The primary contribution of this work lies in integrating pre-defined behavior primitives into imitation learning, thereby addressing the common issue of high sample complexity. By scaffolding manipulation tasks with these primitives, the authors introduce a hierarchical approach where robot tasks are broken down into sequences of primitives. This allows for policies to be learned that focus on sequencing rather than generating low-level motor actions. This drastically reduces the temporal horizon and complexity inherent in traditional imitation learning tasks.

Methodological Insights

PRIME employs a two-step process. The first step involves a trajectory parser to segment task demonstrations into primitive sequences without any human annotations. This segmentation is achieved through an Inverse Dynamics Model (IDM), which identifies the optimal primitive sequences using dynamic programming. The IDM learns the mapping of state transitions to behavior primitives through a self-supervised data collection process, drastically decreasing the requirement for costly human demonstrations.

The second step of the methodology involves training a high-level policy through imitation learning to predict primitive sequences from observations. This is done using behavioral cloning, streamlining the learning process by reducing it to a decision problem over a smaller primitive action space.

Evaluation and Results

The effectiveness of the PRIME framework is validated across both simulated environments and real-world robotic tasks. The paper reports superior performance over baseline imitation learning methods, with enhanced success rates ranging from 10% to 48% higher across various tasks. Notably, PRIME also demonstrates robustness in recovery attempts post-failure by proficiently re-attempting the same primitive type with adjusted parameters.

Implications and Future Directions

The framework exhibits significant practical implications, especially in applications where the efficiency of sample usage is critical. By establishing a scalable method for task decomposition using primitives, the work offers a pathway to more efficient and robust robotic systems that can generalize across different environments and tasks with minimal data requirements.

Theoretically, this paper situates itself within the broader discourse on skill-based learning and hierarchical policy design in robotics, challenging the conventional reliance on large datasets to achieve competent robotic manipulation. Future research directions could involve the automatic discovery and learning of a library of low-level primitives, enabling robots to learn increasingly complex tasks progressively. Moreover, addressing the limitation of sim2real adaptations for IDM training could enhance the system's applicability in unfavorably complex real-world scenarios.

In conclusion, "PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learning" presents a meaningful advancement in the field of imitation learning. By leveraging behavior primitives, it significantly enhances data efficiency, paving the way for more practical robotic applications. The framework's integration of self-supervision elevates the independence of robotic learning, marking a promising step toward autonomous and efficient robotic manipulation.