Suction Leap-Hand: Suction Cups on a Multi-fingered Hand Enable Embodied Dexterity and In-Hand Teleoperation

2025-09-24

AI Summary

Traditional dexterous hands, constrained by anthropomorphic design and force-closure grasping, struggle to surpass human capabilities; moreover, teleoperation demonstrations suffer from instability, and significant sim-to-real gaps hinder deployment. Method: We propose Suction Leap-Hand—the first multi-finger dexterous hand integrating fingertip suction cups—replacing complex multi-point contact with single-point adhesion to simplify stability modeling and data acquisition. Contribution/Results: This design breaks human anatomical constraints, enabling novel single-hand skills such as paper cutting and handwriting. By synergizing suction physics, structural optimization, and demonstration learning, it significantly improves teleoperation robustness and accelerates reinforcement learning training. Experiments demonstrate autonomous execution of tasks previously requiring bimanual coordination and substantially reduce the sim-to-real performance gap.

Technology Category

• Intelligent Robots: Manipulation
• Search and Optimization: Learning to Search
• Machine Learning: Learning with Manifolds

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Dexterous in-hand manipulation remains a foundational challenge in robotics, with progress often constrained by the prevailing paradigm of imitating the human hand. This anthropomorphic approach creates two critical barriers: 1) it limits robotic capabilities to tasks humans can already perform, and 2) it makes data collection for learning-based methods exceedingly difficult. Both challenges are caused by traditional force-closure which requires coordinating complex, multi-point contacts based on friction, normal force, and gravity to grasp an object. This makes teleoperated demonstrations unstable and amplifies the sim-to-real gap for reinforcement learning. In this work, we propose a paradigm shift: moving away from replicating human mechanics toward the design of novel robotic embodiments. We introduce the extbf{S}uction extbf{Leap}-Hand (SLeap Hand), a multi-fingered hand featuring integrated fingertip suction cups that realize a new form of suction-enabled dexterity. By replacing complex force-closure grasps with stable, single-point adhesion, our design fundamentally simplifies in-hand teleoperation and facilitates the collection of high-quality demonstration data. More importantly, this suction-based embodiment unlocks a new class of dexterous skills that are difficult or even impossible for the human hand, such as one-handed paper cutting and in-hand writing. Our work demonstrates that by moving beyond anthropomorphic constraints, novel embodiments can not only lower the barrier for collecting robust manipulation data but also enable the stable, single-handed completion of tasks that would typically require two human hands. Our webpage is https://sites.google.com/view/sleaphand.

Problem

• Overcoming limitations of anthropomorphic robotic hand design paradigms
• Simplifying complex force-closure grasping for stable teleoperation
• Enabling novel dexterous skills impossible for human hands

Innovation

• Multi-fingered hand with integrated fingertip suction cups
• Replaces complex force-closure grasps with single-point adhesion
• Enables stable one-handed teleoperation and new dexterous skills