A Framework for Low-Latency, LLM-driven Multimodal Interaction on the Pepper Robot
arXiv:2603.21013v1 Announce Type: new Abstract: Despite recent advances in integrating Large Language Models (LLMs) into social robotics, two weaknesses persist. First, existing implementations on platforms like Pepper often rely on cascaded Speech-to-Text (STT)->LLM->Text-to-Speech (TTS) pipelines, resulting in high latency and the loss of paralinguistic information. Second, most implementations fail to fully leverage the LLM's capabilities for multimodal perception and agentic control. We present an open-source Android framework for the Pepper robot that addresses these limitations through two key innovations. First, we integrate end-to-end Speech-to-Speech (S2S) models to achieve low-latency interaction while preserving paralinguistic cues and enabling adaptive intonation. Second, we implement extensive Function Calling capabilities that elevate the LLM to an agentic planner, orchestrating robot actions (navigation, gaze control, tablet interaction) and integrating diverse multimod
arXiv:2603.21013v1 Announce Type: new Abstract: Despite recent advances in integrating Large Language Models (LLMs) into social robotics, two weaknesses persist. First, existing implementations on platforms like Pepper often rely on cascaded Speech-to-Text (STT)->LLM->Text-to-Speech (TTS) pipelines, resulting in high latency and the loss of paralinguistic information. Second, most implementations fail to fully leverage the LLM's capabilities for multimodal perception and agentic control. We present an open-source Android framework for the Pepper robot that addresses these limitations through two key innovations. First, we integrate end-to-end Speech-to-Speech (S2S) models to achieve low-latency interaction while preserving paralinguistic cues and enabling adaptive intonation. Second, we implement extensive Function Calling capabilities that elevate the LLM to an agentic planner, orchestrating robot actions (navigation, gaze control, tablet interaction) and integrating diverse multimodal feedback (vision, touch, system state). The framework runs on the robot's tablet but can also be built to run on regular Android smartphones or tablets, decoupling development from robot hardware. This work provides the HRI community with a practical, extensible platform for exploring advanced LLM-driven embodied interaction.
Executive Summary
This article presents a novel, open-source framework for integrating Large Language Models (LLMs) with the Pepper robot to enable low-latency, multimodal interaction. The framework addresses two key limitations of existing implementations: high latency and the failure to leverage the LLM's capabilities for multimodal perception and control. The proposed solution integrates end-to-end Speech-to-Speech (S2S) models and extensive Function Calling capabilities to achieve low-latency interaction and adaptive intonation. This framework provides a practical platform for exploring advanced LLM-driven embodied interaction, with potential applications in human-robot collaboration and social robotics. The framework's modularity and extensibility enable developers to build on existing hardware, decoupling development from robot hardware. This innovation has significant implications for the Human-Robot Interaction (HRI) community and the development of more human-like robots.
Key Points
- ▸ The proposed framework addresses two key limitations of existing LLM implementations: high latency and the failure to leverage the LLM's capabilities for multimodal perception and control.
- ▸ The framework integrates end-to-end S2S models and extensive Function Calling capabilities to achieve low-latency interaction and adaptive intonation.
- ▸ The framework is modular, extensible, and can run on regular Android smartphones or tablets, decoupling development from robot hardware.
Merits
Strength in Multimodal Interaction
The proposed framework enables seamless integration of diverse multimodal feedback (vision, touch, system state), elevating the LLM to an agentic planner, and facilitating more human-like interaction.
Modularity and Extensibility
The framework's modularity and extensibility enable developers to build on existing hardware, decoupling development from robot hardware, and facilitating collaboration across research and development communities.
Practical Implementation
The proposed framework provides a practical platform for exploring advanced LLM-driven embodied interaction, with potential applications in human-robot collaboration and social robotics.
Demerits
Limited Scalability
The proposed framework may struggle with scalability issues when dealing with complex, high-dimensional multimodal data, potentially limiting its application in real-world scenarios.
Dependence on LLMs
The framework's success relies heavily on the performance and capabilities of LLMs, which may be subject to limitations and biases, potentially affecting the framework's overall performance and reliability.
Expert Commentary
The proposed framework represents a significant advancement in the development of more human-like robots, enabling seamless integration of diverse multimodal feedback and adaptive intonation. While the framework's modularity and extensibility are notable strengths, its scalability and dependence on LLMs are potential limitations that must be addressed. The framework's implications for the HRI community and the development of more human-like robots are substantial, with potential applications in social robotics and human-robot collaboration. As the field continues to evolve, policymakers must address the associated concerns regarding data privacy and security, and regulators must revisit existing frameworks governing human-robot interaction.
Recommendations
- ✓ Further research is needed to address scalability issues and develop more robust and reliable LLM-driven embodied interaction.
- ✓ Developers should prioritize modularity and extensibility to facilitate collaboration across research and development communities and enable seamless integration of diverse multimodal feedback.
Sources
Original: arXiv - cs.AI
