In context
By early 2024, service robots were expected to handle complex, unstructured home tasks, yet translating natural language instructions into executable actions remained a bottleneck. Prior work often separated intent recognition from task planning, lacking real-time environmental adaptation. This paper from Shandong University addressed that gap by integrating instruction parsing with environment-aware action generation.
What was reported
Researchers proposed an end-to-end instruction parsing and action sequence generation model for home service robots in intelligent spaces. The model comprises four modules: intent recognition, service strategy generation, ontology-based knowledge interaction, and action sequence generation.
For intent recognition, a joint deep learning model based on BiGRU and attention mechanisms was developed. It uses an improved gating mechanism to capture correlations between task types and keywords, augmented with a prior knowledge vector from a home service knowledge base. The model directly outputs task types and keywords without separate part-of-speech parsing.
Service strategies are generated as intermediate states using a keyword-guided text generation mechanism. An ontology interaction module then matches strategy entities with real-time visual detection and knowledge base information, applying text and attribute similarity for self-correction. If gaps remain, the robot initiates user queries to refine the strategy. Finally, a task planner converts the confirmed strategy into executable atomic actions.
Experiments demonstrated accurate understanding of complex user instructions and generation of reliable action sequences while maintaining friendly interaction. The authors also built a home task understanding dataset (HTUD) with annotated task types and keyword slots.
Why it mattered
This work advanced service robot autonomy by tightly coupling language understanding with environmental context, reducing execution interruptions and improving adaptability in dynamic home settings. The approach offers a practical framework for more intuitive human-robot interaction in domestic automation.
"The proposed method maintains a friendly interaction while accurately understanding complex user instructions, and ultimately generates reliable action sequences that the robot can execute."
Source: 《机器人》期刊 (robot.sia.cn) · Published 2024-02-04 · “智能空间下面向动作序列生成的服务机器人指令解析方法”