Hierarchical Equivariant Policy via Frame Transf

2025-02-09

AI Summary

Existing hierarchical policy learning suffers from coarse-grained interfaces between high- and low-level agents and neglects domain symmetries inherent in robotic manipulation tasks, leading to poor generalization and heavy reliance on demonstration data. This paper proposes HEP, a hierarchical policy framework for complex robotic manipulation. First, it introduces the “frame-passing interface,” where the high-level policy’s output is directly defined as a reference coordinate frame for low-level execution, enabling geometrically consistent cross-layer coordination. Second, it is the first work to theoretically model and guarantee full-system equivariance in hierarchical RL, explicitly embedding rotational and translational symmetries. Third, it integrates group-action modeling, equivariant neural networks, and geometric coordinate transformations. Evaluated in both simulation and real-robot settings, HEP achieves state-of-the-art performance, significantly improving long-horizon task reasoning accuracy and fine-grained manipulation stability.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recent advances in hierarchical policy learning highlight the advantages of decomposing systems into high-level and low-level agents, enabling efficient long-horizon reasoning and precise fine-grained control. However, the interface between these hierarchy levels remains underexplored, and existing hierarchical methods often ignore domain symmetry, resulting in the need for extensive demonstrations to achieve robust performance. To address these issues, we propose Hierarchical Equivariant Policy (HEP), a novel hierarchical policy framework. We propose a frame transfer interface for hierarchical policy learning, which uses the high-level agent's output as a coordinate frame for the low-level agent, providing a strong inductive bias while retaining flexibility. Additionally, we integrate domain symmetries into both levels and theoretically demonstrate the system's overall equivariance. HEP achieves state-of-the-art performance in complex robotic manipulation tasks, demonstrating significant improvements in both simulation and real-world settings.

Problem

• Addresses interface between hierarchical policy levels
• Incorporates domain symmetry for robust performance
• Enhances robotic manipulation through equivariant policy

Innovation

• Hierarchical Equivariant Policy framework
• Frame transfer interface integration
• Domain symmetry incorporation