Immersive Social Interaction with VR and LLM-Assisted Humanoids

2026-07-08

AI Summary

Existing teleoperation interfaces for humanoid robots often impose excessive physical or cognitive demands, hindering natural and efficient remote interaction. This work proposes an immersive teleoperation framework that uniquely integrates first-person visual feedback from the Apple Vision Pro, VR-based hand tracking, voice-driven large language model (LLM) command parsing, and bidirectional social interaction. The system maps dexterous hand motions through inverse kinematics and PD control while simultaneously capturing multimodal data—including gaze, speech, and motion—for subsequent imitation learning. Experiments on the Unitree H1 platform demonstrate that novice users, after minimal familiarization, achieve success rates of 80% in object manipulation and 70% in a social cube-passing task, validating the effectiveness and potential of this approach for natural-language-driven full-body teleoperation.

Abstract

Humanoid robots can extend human presence to remote, constrained, or hazardous environments, but existing teleoperation interfaces often require physically demanding motion tracking or cognitively demanding low-level control. This paper presents an immersive teleoperation framework that integrates voice-controlled locomotion, VR-based manipulation, and bidirectional social interaction for whole-body humanoid control. Using Apple Vision Pro, the operator receives egocentric visual feedback, issues natural-language locomotion commands, and teleoperates the robot's arms and dexterous hands through wrist and finger tracking. An LLM-assisted voice-control module converts spoken instructions into high-level locomotion commands, while the manipulation module retargets human hand motions to the robot through inverse kinematics and PD control. The system also records multimodal data, including egocentric RGB observations, voice/text commands, joint states, hand motions, and eye-gaze signals, supporting future imitation learning and autonomy. We evaluate the framework on a Unitree H1 humanoid equipped with dexterous hands in manipulation and social interaction tasks. Results show that novice users can successfully operate the system after brief familiarization, achieving 80\% success in object manipulation and 70\% success in a social cube-passing task. These results demonstrate the potential of immersive, language-assisted teleoperation as an accessible interface for humanoid interaction, remote assistance, and multimodal data collection.

Problem

• teleoperation
• humanoid robots
• immersive interaction
• social interaction
• user interface

Innovation

• immersive teleoperation
• LLM-assisted control
• VR-based manipulation
• multimodal data collection
• humanoid robotics