Cognitive Approach to Hierarchical Task Selection for Human-Robot Interaction in Dynamic Environments

TL;DR

This paper presents a semantic memory-based hierarchical control system enabling a robot to interpret human cues and environmental context for task selection, demonstrated through tea and sandwich making in dynamic settings.

Contribution

The authors extend hierarchical robot control architecture with semantic memory to improve task understanding and execution based on human feedback and environmental cues.

Findings

Robot correctly identified context-based tasks in all scenarios.

The system effectively used relevant objects for task execution.

Hierarchical control with semantic memory enhances human-robot collaboration.

Abstract

In an efficient and flexible human-robot collaborative work environment, a robot team member must be able to recognize both explicit requests and implied actions from human users. Identifying "what to do" in such cases requires an agent to have the ability to construct associations between objects, their actions, and the effect of actions on the environment. In this regard, semantic memory is being introduced to understand the explicit cues and their relationships with available objects and required skills to make "tea" and "sandwich". We have extended our previous hierarchical robot control architecture to add the capability to execute the most appropriate task based on both feedback from the user and the environmental context. To validate this system, two types of skills were implemented in the hierarchical task tree: 1) Tea making skills and 2) Sandwich making skills. During the conversation between the robot and the human, the robot was able to determine the hidden context using ontology and began to act accordingly. For instance, if the person says "I am thirsty" or "It is cold outside" the robot will start to perform the tea-making skill. In contrast, if the person says, "I am hungry" or "I need something to eat", the robot will make the sandwich. A humanoid robot Baxter was used for this experiment. We tested three scenarios with objects at different positions on the table for each skill. We observed that in all cases, the robot used only objects that were relevant to the skill.