Robustifying Long-term Human-Robot Collaboration through a Multimodal and Hierarchical Framework (2411.15711v2)

Abstract: Long-term Human-Robot Collaboration (HRC) is crucial for enabling flexible manufacturing systems and integrating companion robots into daily human environments over extended periods. This paper identifies several key challenges for such collaborations, such as accurate recognition of human plan, robustness to disturbances, operational efficiency, adaptability to diverse user behaviors, and sustained human satisfaction. To address these challenges, we model the long-term HRC task through a hierarchical task graph and presents a novel multimodal and hierarchical framework to enable robots to better assist humans to advance on the task graph. In particular, the proposed multimodal framework integrates visual observations with speech commands to facilitate intuitive and flexible human-robot interactions. Additionally, our hierarchical designs for both human pose detection and plan prediction allow better understanding of human behaviors and significantly enhance system accuracy, robustness and flexibility. Moreover, an online adaptation mechanism enables real-time adjustment to diverse user behaviors. We deploy the proposed framework to KINOVA GEN3 robot and conduct extensive user studies on real-world long-term HRC assembly scenarios. Experimental results show that our approaches reduce task completion time by 15.9%, achieves an average task success rate of 91.8% and an overall user satisfaction score of 84% in long-term HRC tasks, showcasing its applicability in enhancing real-world long-term HRC.

• The paper proposes a novel hierarchical and multimodal framework combining visual and auditory inputs to significantly enhance the robustness and efficiency of long-term human-robot collaboration.
• The multimodal approach improves human intention recognition and creates a richer, more intuitive collaborative interface compared to single-modality systems.
• Empirical validation using a KINOVA GEN3 robot and user studies demonstrated improved task completion rates, higher prediction accuracy, and enhanced user satisfaction in real-world scenarios.

Robustifying Long-term Human-Robot Collaboration through a Hierarchical and Multimodal Framework

The complexities involved in Long-term Human-Robot Collaboration (HRC) span various challenges such as robust intention recognition, adaptability, and efficiency in dynamic environments. The paper under discussion proposes a novel framework that amalgamates both multimodal perception and hierarchical planning to advance the robustness and efficiency of HRC systems over extended periods. This framework is specifically designed to address four critical issues: accurate understanding of human intentions, resilience to environmental noise, collaboration efficiency, and adaptability to diverse user behaviors.

The proposed architecture integrates visual observations with auditory inputs, creating a richer interaction modality that surpasses the limitations of using either modality in isolation. This multimodal approach not only facilitates a more comprehensive understanding of human intentions but also enriches the collaborative interface between humans and robots, making interactions more intuitive and flexible. Visual inputs are primarily processed for pose detection and intention prediction, while auditory cues refine these predictions by providing contextual clarity, especially in tasks where visual cues might be ambiguous.

Additionally, the framework employs a hierarchical structure in its planning modules, prominently in human detection and intention prediction. This hierarchical design plays a crucial role in minimizing disturbances, especially in scenarios featuring multiple humans, thereby enhancing the accuracy of detecting relevant human actions. The human intention prediction model is further optimized through online adaptation, tailoring the system to align more closely with individual human behaviors and preferences in real-time. This adaptability is critical for maintaining the efficacy of HRC systems across diverse user interactions.

Deployment of this framework on a KINOVA GEN3 robot, coupled with user studies in real-world, long-term HRC tasks, provides empirical evidence of its effectiveness. The experimental results highlight significant improvements in system robustness, task completion rates, and efficiency. For instance, the multimodal framework demonstrated a shorter task completion time and higher accuracy in human action prediction when compared to vision-only or audio-only systems. The user feedback also corroborated the hypothesis that the framework enhances user satisfaction by facilitating a more seamless and responsive collaborative experience.

This research presents substantial implications for the future development of HRC systems in both industrial and domestic settings. By advancing a robust and flexible framework, it sets the foundation for deploying companion robots capable of engaging in complex, long-duration tasks in noisy, multi-user environments. Future research avenues could explore enhancing the scalability of this framework across varied robotic platforms and further refining user-specific adaptations to cater to even more personalized interactions.

The paper makes significant contributions to the domain of human-robot interaction by offering a comprehensive, integrated solution to enhance long-term collaboration. While the results are promising, the extension of this work to more complex, multi-agent systems could provide additional insights into the development of autonomous collaborative systems that operate harmoniously alongside humans in diverse scenarios.