Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions (1710.06280v2)

Abstract: Comprehension of spoken natural language is an essential component for robots to communicate with human effectively. However, handling unconstrained spoken instructions is challenging due to (1) complex structures including a wide variety of expressions used in spoken language and (2) inherent ambiguity in interpretation of human instructions. In this paper, we propose the first comprehensive system that can handle unconstrained spoken language and is able to effectively resolve ambiguity in spoken instructions. Specifically, we integrate deep-learning-based object detection together with natural language processing technologies to handle unconstrained spoken instructions, and propose a method for robots to resolve instruction ambiguity through dialogue. Through our experiments on both a simulated environment as well as a physical industrial robot arm, we demonstrate the ability of our system to understand natural instructions from human operators effectively, and how higher success rates of the object picking task can be achieved through an interactive clarification process.

Overview of "Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions"

The paper presents a comprehensive system designed to facilitate human-robot interaction by enabling robots to comprehend and execute unconstrained spoken language instructions for object retrieval tasks. The research addresses key challenges associated with the interpretation of complex and ambiguous spoken commands, integrating advanced deep learning methods in object detection and natural language processing to overcome these hurdles.

Key Contributions and Methodology

This paper introduces a robust framework enabling robots to interact with human operators using natural language, encompassing the entire process from instruction comprehension to physical task execution. The proposed methodology involves:

• Advanced Object Detection Techniques: Leveraging a modified Single Shot Multibox Detector (SSD) architecture, the system can detect candidate objects without relying on explicit class labels. This approach enhances the generalization capabilities of the robot, allowing it to recognize unseen objects in real-world environments.
• Natural Language Processing Integration: A crucial component of the system is its ability to disambiguate spoken instructions via an interactive dialogue mechanism. By integrating referring expression comprehension, the system can refine its understanding through follow-up queries, thereby improving task success rates.
• Comprehensive Evaluation: The system's efficacy was validated through experiments in both simulated and physical environments, demonstrating significant success in interpreting ambiguous instructions and executing object-picking tasks. An average success rate of 73.1% was achieved for end-to-end object picking with a physical robot, highlighting the system's robustness and real-world applicability.

Numerical Results and Implications

The paper reports several strong numerical outcomes:

• Object detection achieved an average precision of 98.6%, while destination box selection accomplished an accuracy of 95.5% in task execution.
• The target object selection component reached a top-2 accuracy of 95.5%, emphasizing the system’s ability to handle ambiguous instructions effectively through clarification dialogues.
• Notably, the error reduction in object selection tasks was approximately 39.2% when utilizing the interactive clarification process.

These results underscore the practicality of the framework in enhancing human-robot interaction fidelity. The system's ability to resolve ambiguities iteratively signifies a pivotal step towards more intuitive and effective human-robot interfaces.

Speculation on Future Developments

This research offers foundational insights for the future development of AI-driven robotic systems, laying the groundwork for more complex task automation in varied real-world settings. Future advancements could include:

• Cross-Linguistic Capabilities: Expanding the system's language comprehension to support multiple languages, leveraging shared models across linguistic boundaries.
• Enhanced End-Effector Technology: Further refining grasping strategies to handle diverse objects with increased reliability and precision, potentially incorporating adaptive gripper mechanisms alongside vacuum systems.
• Scalable Training Datasets: Developing larger, more diverse datasets to train models capable of handling a wider array of natural language nuances and situational contexts.

The implications of this paper extend to numerous domains, including industrial automation, assistive robotics, and consumer robotics, where seamless human-machine communication is paramount. As AI technology progresses, such systems will be integral to achieving enhanced operational efficiency and user experience.