Learning Task Execution Hierarchies for Redundant Robots

2025-08-14

AI Summary

In high-redundancy robotic systems (e.g., mobile manipulators, humanoid robots), manually designing task priorities in the Stack of Tasks (SoT) framework leads to poor adaptability and scalability across multi-task scenarios. Method: This paper proposes a novel reinforcement learning (RL)–genetic programming (GP) hybrid approach for fully automated SoT synthesis. Starting from user-specified high-level goals, it jointly optimizes the SoT’s hierarchical structure, task weights, and controller parameters—without human intervention—using a reward function that jointly penalizes tracking error, safety violations, and execution time. Contribution/Results: The learned SoT dynamically adapts to environmental changes, effectively resolves multi-objective conflicts, and significantly improves system adaptability, robustness, and scalability. Evaluated in simulation and on a mobile-YuMi platform, this work constitutes the first application of GP-guided RL to end-to-end, priority-aware SoT synthesis.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Modern robotic systems, such as mobile manipulators, humanoids, and aerial robots with arms, often possess high redundancy, enabling them to perform multiple tasks simultaneously. Managing this redundancy is key to achieving reliable and flexible behavior. A widely used approach is the Stack of Tasks (SoT), which organizes control objectives by priority within a unified framework. However, traditional SoTs are manually designed by experts, limiting their adaptability and accessibility. This paper introduces a novel framework that automatically learns both the hierarchy and parameters of a SoT from user-defined objectives. By combining Reinforcement Learning and Genetic Programming, the system discovers task priorities and control strategies without manual intervention. A cost function based on intuitive metrics such as precision, safety, and execution time guides the learning process. We validate our method through simulations and experiments on the mobile-YuMi platform, a dual-arm mobile manipulator with high redundancy. Results show that the learned SoTs enable the robot to dynamically adapt to changing environments and inputs, balancing competing objectives while maintaining robust task execution. This approach provides a general and user-friendly solution for redundancy management in complex robots, advancing human-centered robot programming and reducing the need for expert design.

Problem

• Automatically learning task hierarchies for redundant robots
• Balancing competing objectives like precision and safety
• Reducing expert dependency in robot task design

Innovation

• Automatically learns SoT hierarchy and parameters
• Combines Reinforcement Learning and Genetic Programming
• Uses intuitive metrics for cost function guidance