APPROVE: Visual End-User-in-the-Loop Robot Programming with LLMs

Programming robots remains challenging for non-experts, as traditional methods require expert knowledge and even block-based interfaces often lack flexibility. Recent work has explored Large Language Models (LLMs) to automatically generate robot programs from natural language, but these systems remain limited by a lack of transparency, missing mechanisms to ensure alignment with user intent, and little support for reuse. We present APPROVE (AI-Powered Programming for Robots with Visual End-User Feedback), an LLM-based multi-modal end-user programming framework that integrates natural language input with a block-based interface and an explicit user confirmation step. Generated programs are visualized using a block-based interface in Blockly, allowing users to confirm, modify, or reject them before execution. Confirmed functions are stored in a library for reuse, gradually building a set of reliable program components. Our approach contributes a human-centered design for LLM-based robot programming that emphasizes user trust, intent alignment, and reusability.

Intuitive Human-Robot Interaction: Development and Evaluation of a Gesture-Based User Interface for Object Selection

Gestures are a natural form of communication between humans and can also be leveraged for human-robot interaction. This work presents a gesture-based user interface for object selection using pointing and click gestures. An experiment with 20 participants evaluates accuracy and selection time, demonstrating the potential for efficient collaboration.

Automating Manual Tasks through Intuitive Robot Programming and Cognitive Robotics

This paper presents a novel concept for intuitive end-user programming of robots, inspired by natural interaction between humans. Natural language and supportive gestures are translated into robot programs using large language models (LLMs) and computer vision (CV). Through equally natural system feedback in the form of clarification questions and visual representations, the generated program can be reviewed and adjusted, thereby ensuring safety, transparency, and user acceptance.