ToS: Tree-of-Skill Reinforcement Learning driven by Behavior Tree for Long-Horizon Manipulation

Behavior Trees (BTs) are known for their modular nature, making them a promising control architecture for long-horizon manipulation tasks. BT planning leverages high-level symbolic behavior models to effectively generate reliable BTs. However, low-level skill design often involves dependencies between skills that are difficult to model or reason about using symbols, such as precise object poses. Existing skill chaining methods typically focus on connecting a predefined sequence of skills but struggle to handle multiple possible subsequent branches. This paper proposes Tree-of-Skill (ToS), the first neural-symbolic framework for dynamic skill chaining in BT-driven Reinforcement Learning (RL). In ToS, subsequent skills are predicted using behavior models during BT execution, and the current skill is dynamically chained to its successor via the goal generated by the successor’s guiding network. Guidance is applied when two adjacent skills have dependencies, which may be explicitly modeled or implicit. We introduce the goal mask for skills to support heterogeneous goal spaces or to enable execute without a successor-provided goal. The training process of ToS includes goal-conditioned skill learning, guiding network learning, and optional task-specific fine-tuning. Experiments are conducted on a robot equipped with two Realman RM75-6F arms and two PsiBot G0-R hands. We train 8 skills in simulation and evaluate them on 7 two-step tasks and 5 long-horizon (3–5 steps) tasks. Results show that ToS generally achieves higher success rates than the baselines, particularly on unseen tasks. Ablation studies further demonstrate the effectiveness of the guiding network. Finally, real-world deployment confirms the practical applicability of ToS for long-horizon manipulation.