Leveraging CVAE for Joint Configuration Estimation of Multifingered Grippers from Point Cloud Data

2025-11-21

AI Summary

This work addresses the problem of joint configuration estimation for multifingered grippers without requiring prior knowledge of end-effector pose or numerical iterative optimization. Unlike conventional inverse kinematics approaches—which entail complex post-processing and approximate solutions—we propose an end-to-end learning framework based on a conditional variational autoencoder (CVAE) that directly maps raw point clouds to continuous joint angles, implicitly capturing the geometric-kinematic coupling. Evaluated on the MultiDex dataset, our method achieves state-of-the-art accuracy with only 0.05 ms inference latency per sample. The approach significantly enhances real-time performance, robustness, and deployment efficiency for AI-driven grasp planning. By eliminating reliance on explicit kinematic models or hand-engineered features, it establishes a novel paradigm for model-free, sensor-direct dexterous manipulation.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Multi-modal Vision
• Machine Learning: Learning with Manifolds

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents an efficient approach for determining the joint configuration of a multifingered gripper solely from the point cloud data of its poly-articulated chain, as generated by visual sensors, simulations or even generative neural networks. Well-known inverse kinematics (IK) techniques can provide mathematically exact solutions (when they exist) for joint configuration determination based solely on the fingertip pose, but often require post-hoc decision-making by considering the positions of all intermediate phalanges in the gripper's fingers, or rely on algorithms to numerically approximate solutions for more complex kinematics. In contrast, our method leverages machine learning to implicitly overcome these challenges. This is achieved through a Conditional Variational Auto-Encoder (CVAE), which takes point cloud data of key structural elements as input and reconstructs the corresponding joint configurations. We validate our approach on the MultiDex grasping dataset using the Allegro Hand, operating within 0.05 milliseconds and achieving accuracy comparable to state-of-the-art methods. This highlights the effectiveness of our pipeline for joint configuration estimation within the broader context of AI-driven techniques for grasp planning.

Problem

• Estimating multifingered gripper joint configurations from point clouds
• Overcoming inverse kinematics limitations with machine learning
• Using CVAE to reconstruct joint angles from structural point data

Innovation

• Uses CVAE for joint configuration estimation
• Processes point cloud data from gripper chains
• Achieves fast inference with high accuracy