Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots

• URL: http://arxiv.org/abs/2604.12909v1
• Date: Tue, 14 Apr 2026 15:57:27 GMT
• Title: Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots
• Abstract summary: We propose Tree Learning, a multi-skill continual learning framework for humanoid robots.<n>The framework adopts a root-branch hierarchical parameter inheritance mechanism, providing motion priors for branch skills.<n>A task-level reward shaping strategy is also proposed to accelerate skill convergence.
• Score: 1.282675419968047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastrophic forgetting has become a key challenge in embodied intelligence. Existing approaches either rely on complex topology adjustments in Mixture-of-Experts (MoE) models or require training extremely large-scale models, making lightweight deployment difficult. To address this, we propose Tree Learning, a multi-skill continual learning framework for humanoid robots. The framework adopts a root-branch hierarchical parameter inheritance mechanism, providing motion priors for branch skills through parameter reuse to fundamentally prevent catastrophic forgetting. A multi-modal feedforward adaptation mechanism combining phase modulation and interpolation is designed to support both periodic and aperiodic motions. A task-level reward shaping strategy is also proposed to accelerate skill convergence. Unity-based simulation experiments show that, in contrast to simultaneous multi-task training, Tree Learning achieves higher rewards across various representative locomotion skills while maintaining a 100% skill retention rate, enabling seamless multi-skill switching and real-time interactive control. We further validate the performance and generalization capability of Tree Learning on two distinct Unity-simulated tasks: a Super Mario-inspired interactive scenario and autonomous navigation in a classical Chinese garden environment.

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