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Contact-based Grasp Control and Inverse Kinematics for a Five-fingered Robotic Hand

Robinson Umeike

TL;DR

The paper presents a contact-based grasp control framework for a five-fingered robotic hand (DexHand v2) in PyBullet, combining contact-point optimization with inverse kinematics to achieve stable grasps validated by force closure and perturbation testing. A two-level architecture enables pre-grasp positioning, real-time contact optimization, and stability monitoring, achieving high movement efficiency ($\eta$) and precise end-effector positioning at a 240 Hz simulation rate. Key results show $\eta$ in the range $0.966$ to $0.996$ for non-thumb fingers and $0.879$ for the thumb, with positional errors of $0.0267$–$0.0283$ m for non-thumb digits and $0.0519$ m for the thumb, demonstrating rapid stabilization and robust contact configurations. The work validates the approach on the YCB CrackerBox within a PyBullet environment and identifies future directions in improving thumb opposition movements and horizontal-plane control for hardware deployment.

Abstract

This paper presents an implementation and analysis of a five-fingered robotic grasping system that combines contact-based control with inverse kinematics solutions. Using the PyBullet simulation environment and the DexHand v2 model, we demonstrate a comprehensive approach to achieving stable grasps through contact point optimization with force closure validation. Our method achieves movement efficiency ratings between 0.966-0.996 for non-thumb fingers and 0.879 for the thumb, while maintaining positional accuracy within 0.0267-0.0283m for non-thumb digits and 0.0519m for the thumb. The system demonstrates rapid position stabilization at 240Hz simulation frequency and maintains stable contact configurations throughout the grasp execution. Experimental results validate the effectiveness of our approach, while also identifying areas for future enhancement in thumb opposition movements and horizontal plane control.

Contact-based Grasp Control and Inverse Kinematics for a Five-fingered Robotic Hand

TL;DR

The paper presents a contact-based grasp control framework for a five-fingered robotic hand (DexHand v2) in PyBullet, combining contact-point optimization with inverse kinematics to achieve stable grasps validated by force closure and perturbation testing. A two-level architecture enables pre-grasp positioning, real-time contact optimization, and stability monitoring, achieving high movement efficiency () and precise end-effector positioning at a 240 Hz simulation rate. Key results show in the range to for non-thumb fingers and for the thumb, with positional errors of – m for non-thumb digits and m for the thumb, demonstrating rapid stabilization and robust contact configurations. The work validates the approach on the YCB CrackerBox within a PyBullet environment and identifies future directions in improving thumb opposition movements and horizontal-plane control for hardware deployment.

Abstract

This paper presents an implementation and analysis of a five-fingered robotic grasping system that combines contact-based control with inverse kinematics solutions. Using the PyBullet simulation environment and the DexHand v2 model, we demonstrate a comprehensive approach to achieving stable grasps through contact point optimization with force closure validation. Our method achieves movement efficiency ratings between 0.966-0.996 for non-thumb fingers and 0.879 for the thumb, while maintaining positional accuracy within 0.0267-0.0283m for non-thumb digits and 0.0519m for the thumb. The system demonstrates rapid position stabilization at 240Hz simulation frequency and maintains stable contact configurations throughout the grasp execution. Experimental results validate the effectiveness of our approach, while also identifying areas for future enhancement in thumb opposition movements and horizontal plane control.

Paper Structure

This paper contains 30 sections, 12 equations, 4 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. System Parameters
  4. Primary Objectives
  5. Contact Point Satisfaction
  6. Inverse Kinematics
  7. Constraints
  8. Physical Constraints
  9. Task Constraints
  10. Evaluation Metrics
  11. I. Distance to Target.
  12. II. Movement Efficiency ($\eta$).
  13. III. Spatial Distribution Error.
  14. IV. Temporal Performance.
  15. Related Work
  16. ...and 15 more sections

Figures (4)

  • Figure 1: Key performance results for all five fingers
  • Figure 2: Evolution of grasp stability during DexHand v2 manipulation of YCB CrackerBox, demonstrating improvement from initial to optimized contact configuration
  • Figure 3: Time-Series Analysis of Finger Position Trajectories showing distinct thumb opposition and stratified finger positioning for grasp formation.
  • Figure 4: Spatial Distribution Error Analysis: (Left) Error distribution across X, Y, Z coordinates; (Right) Per-finger absolute error magnitudes showing thumb's higher deviation (0.0519m) compared to other fingers (0.0267-0.0283m).