Hanqian Luo

In the field of learning from demonstration (LfD), enabling robots to generalize learned manipulation skills to novel scenarios for long-horizon tasks remains challenging. Specifically, it is still difficult for robots to adapt the learned skills to new environments with different task and motion requirements, especially in long-horizon, multistage scenarios with intricate constraints. This article proposes a novel hierarchical framework, called BT-TL-DMPs, that integrates behavior tree (BT), temporal logic (TL), and dynamical movement primitives (DMPs) to address this problem. Within this framework, signal temporal logic (STL) is employed to formally specify complex, long-horizon task requirements and constraints. These STL specifications are systematically transformed to generate reactive and modular BTs for high-level decision-making task structure. An STL-constrained DMP optimization method is proposed to optimize the DMP forcing term, allowing the learned motion primitives to adapt flexibly while satisfying intricate spatiotemporal requirements and, crucially, preserving the essential dynamics learned from demonstrations. The framework is validated through simulations demonstrating generalization capabilities under various STL constraints and real-world experiments on several long-horizon robotic manipulation tasks, where it significantly improves the success rate from 0.25 to 0.85, compared with the baseline. The results demonstrate that the proposed framework effectively bridges the symbolic-motion gap, enabling more reliable and generalizable autonomous manipulation for complex robotic tasks.

Language-conditioned manipulation (LCM) couples vision, language, and control to enable natural instruction following, making it a promising direction in embodied AI research. Researchers have combined imitation learning (IL) with reinforcement learning (RL), where IL provides sample-efficient initialization and RL refines the policy through feedback to maximize long-term returns. Despite progress, safe LCM execution faces two challenges: (1) a modality gap that disrupts semantic-spatial correspondence, degrading action quality, and (2) sparse safety costs in high-dimensional states that make constraint estimation and semantic–safety alignment difficult. In this paper, we present Semantic-Aware Behavior Optimization with Safety Reinforcement Feedback, a novel framework that mitigates multimodal misalignment through context-aware initialization and enforces semantic–safety consistency during execution. We introduce spatial–semantic priors to align language goals with spatial observations and embed safety cues. Specifically, a goal prior aligns modalities for context-aware initialization during behavioral cloning, while a constraint prior combined with a feasibility metric provides learnable supervision for safety reinforcement fine-tuning. Evaluations show our framework achieves a 6.7% relative improvement in simulation and effectively reduces safety violations in real-world tasks across diverse scenarios.