Bidirectional Human-Robot Communication for Physical Human-Robot Interaction

2026-01-15

AI Summary

This work addresses the limited adaptability to user preferences and lack of behavioral transparency in current physical human-robot interaction systems. The authors propose BRIDGE, a novel framework that enables real-time, bidirectional modulation of robotic trajectories—including position, velocity, and force—through natural language. BRIDGE leverages a large language model to interpret user instructions and dialogue history, dynamically adjusting robot behavior via real-time trajectory planning. To enhance interaction transparency, the system incorporates proactive verbal feedback from the robot. Evaluated in three assistive tasks with 18 older adults, BRIDGE significantly outperformed a no-feedback baseline, demonstrating marked improvements in perceived intuitiveness and transparency of interaction.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Effective physical human-robot interaction requires systems that are not only adaptable to user preferences but also transparent about their actions. This paper introduces BRIDGE, a system for bidirectional human-robot communication in physical assistance. Our method allows users to modify a robot's planned trajectory -- position, velocity, and force -- in real time using natural language. We utilize a large language model (LLM) to interpret any trajectory modifications implied by user commands in the context of the planned motion and conversation history. Importantly, our system provides verbal feedback in response to the user, either assuring any resulting changes or posing a clarifying question. We evaluated our method in a user study with 18 older adults across three assistive tasks, comparing BRIDGE to an ablation without verbal feedback and a baseline. Results show that participants successfully used the system to modify trajectories in real time. Moreover, the bidirectional feedback led to significantly higher ratings of interactivity and transparency, demonstrating that the robot's verbal response is critical for a more intuitive user experience. Videos and code can be found on our project website: https://bidir-comm.github.io/

Problem

• human-robot interaction
• bidirectional communication
• physical assistance
• transparency
• real-time trajectory modification

Innovation

• bidirectional communication
• large language model
• physical human-robot interaction
• real-time trajectory modification
• natural language feedback