From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for Telemanipulation

2025-02-04

AI Summary

This work addresses natural teleoperation of kinematically redundant robotic arms. We propose a real-time imitation and generalization framework based on upper-limb human pose mapping. Methodologically, we introduce GRU-VAE for modeling the robot’s configuration manifold—integrating kinematic embedding with real-time decoding—to achieve end-to-end learning from human poses to high-dimensional robot configurations. Our key contributions are: (1) zero-shot generalization—generating kinematically feasible, diverse, and plausible robot configurations for unseen human poses during training; and (2) balanced real-time performance and dexterity, with low system latency suitable for dynamic human–robot interaction. Experiments demonstrate significant improvements in teleoperation naturalness, adaptability to novel tasks, and deployment robustness compared to prior approaches.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

This paper presents a teleoperation system for controlling a redundant degree of freedom robot manipulator using human arm gestures. We propose a GRU-based Variational Autoencoder to learn a latent representation of the manipulator's configuration space, capturing its complex joint kinematics. A fully connected neural network maps human arm configurations into this latent space, allowing the system to mimic and generate corresponding manipulator trajectories in real time through the VAE decoder. The proposed method shows promising results in teleoperating the manipulator, enabling the generation of novel manipulator configurations from human features that were not present during training.

Problem

• Teleoperation system for robotic control
• GRU-based Variational Autoencoder for kinematics
• Neural network mapping human to robot movements

Innovation

• GRU-based Variational Autoencoder
• Latent space mapping
• Real-time trajectory generation