TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types

2025-07-02

AI Summary

Dexterous teleoperation is constrained by conventional hand retargeting paradigms, limiting the exploitation of dexterous hands’ structural advantages beyond human anatomy. Method: We propose TypeTele, a type-guided dexterous teleoperation system. Its core innovation is the first formalization of dexterous manipulation types—e.g., “pinch-rotate” and “envelop-press”—as reusable, extensible action primitives, coupled with multimodal large language models (MLLMs) for task-semantic-driven automatic type retrieval and matching. TypeTele integrates task instruction understanding, typed action modeling, and a unified real-robot teleoperation/imitation learning framework. Contribution/Results: Experiments demonstrate that TypeTele significantly improves success rates on complex manipulation tasks. It exhibits superior task adaptability in real-world settings and enables action generalization beyond anthropomorphic gesture constraints—achieving dexterous behaviors unattainable via human hand motion retargeting alone.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Interaction Techniques and Devices

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Dexterous teleoperation plays a crucial role in robotic manipulation for real-world data collection and remote robot control. Previous dexterous teleoperation mostly relies on hand retargeting to closely mimic human hand postures. However, these approaches may fail to fully leverage the inherent dexterity of dexterous hands, which can execute unique actions through their structural advantages compared to human hands. To address this limitation, we propose TypeTele, a type-guided dexterous teleoperation system, which enables dexterous hands to perform actions that are not constrained by human motion patterns. This is achieved by introducing dexterous manipulation types into the teleoperation system, allowing operators to employ appropriate types to complete specific tasks. To support this system, we build an extensible dexterous manipulation type library to cover comprehensive dexterous postures used in manipulation tasks. During teleoperation, we employ a MLLM (Multi-modality Large Language Model)-assisted type retrieval module to identify the most suitable manipulation type based on the specific task and operator commands. Extensive experiments of real-world teleoperation and imitation learning demonstrate that the incorporation of manipulation types significantly takes full advantage of the dexterous robot's ability to perform diverse and complex tasks with higher success rates.

Problem

• Enabling dexterous hands to surpass human motion constraints
• Creating a library for diverse dexterous manipulation postures
• Improving task success rates with type-guided teleoperation

Innovation

• Type-guided dexterous teleoperation system
• Extensible dexterous manipulation type library
• MLLM-assisted type retrieval module