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Underactuated Robotic Hand with Grasp State Estimation Using Tendon-Based Proprioception

Jae-Hyun Lee, Jonghoo Park, Kyu-Jin Cho

TL;DR

This work addresses grasp-state estimation for underactuated anthropomorphic hands using tendon-based proprioception. It introduces the MPC-SEA, a compact sensorized actuator that provides tendon tension and excursion signals, enabling an energy-based finger model to infer proximal/distal contact timing, joint angles, relative stiffness, and external disturbances from a single sensing modality. The authors implement a rule-based estimator and demonstrate finger-level and hand-level capabilities, including grasp posture reconstruction, safe grip regulation for deformable objects, disturbance detection, and blind object recognition, all without vision or tactile sensors. The results show high estimation accuracy, reliable sensing over extensive cyclic use, and practical grasp functionalities, illustrating the potential for compact, robust manipulation in sensorless hands.

Abstract

Anthropomorphic underactuated hands are valued for their structural simplicity and inherent adaptability. However, the uncertainty arising from interdependent joint motions makes it challenging to capture various grasp states during hand-object interaction without increasing structural complexity through multiple embedded sensors. This motivates the need for an approach that can extract rich grasp-state information from a single sensing source while preserving the simplicity of underactuation. This study proposes an anthropomorphic underactuated hand that achieves comprehensive grasp state estimation, using only tendon-based proprioception provided by series elastic actuators (SEAs). Our approach is enabled by the design of a compact SEA with high accuracy and reliability that can be seamlessly integrated into sensorless fingers. By coupling accurate proprioceptive measurements with potential energy-based modeling, the system estimates multiple key grasp state variables, including contact timing, joint angles, relative object stiffness, and external disturbances. Finger-level experimental validations and extensive hand-level grasp functionality demonstrations confirmed the effectiveness of the proposed approach. These results highlight tendon-based proprioception as a compact and robust sensing modality for practical manipulation without reliance on vision or tactile feedback.

Underactuated Robotic Hand with Grasp State Estimation Using Tendon-Based Proprioception

TL;DR

This work addresses grasp-state estimation for underactuated anthropomorphic hands using tendon-based proprioception. It introduces the MPC-SEA, a compact sensorized actuator that provides tendon tension and excursion signals, enabling an energy-based finger model to infer proximal/distal contact timing, joint angles, relative stiffness, and external disturbances from a single sensing modality. The authors implement a rule-based estimator and demonstrate finger-level and hand-level capabilities, including grasp posture reconstruction, safe grip regulation for deformable objects, disturbance detection, and blind object recognition, all without vision or tactile sensors. The results show high estimation accuracy, reliable sensing over extensive cyclic use, and practical grasp functionalities, illustrating the potential for compact, robust manipulation in sensorless hands.

Abstract

Anthropomorphic underactuated hands are valued for their structural simplicity and inherent adaptability. However, the uncertainty arising from interdependent joint motions makes it challenging to capture various grasp states during hand-object interaction without increasing structural complexity through multiple embedded sensors. This motivates the need for an approach that can extract rich grasp-state information from a single sensing source while preserving the simplicity of underactuation. This study proposes an anthropomorphic underactuated hand that achieves comprehensive grasp state estimation, using only tendon-based proprioception provided by series elastic actuators (SEAs). Our approach is enabled by the design of a compact SEA with high accuracy and reliability that can be seamlessly integrated into sensorless fingers. By coupling accurate proprioceptive measurements with potential energy-based modeling, the system estimates multiple key grasp state variables, including contact timing, joint angles, relative object stiffness, and external disturbances. Finger-level experimental validations and extensive hand-level grasp functionality demonstrations confirmed the effectiveness of the proposed approach. These results highlight tendon-based proprioception as a compact and robust sensing modality for practical manipulation without reliance on vision or tactile feedback.

Paper Structure

This paper contains 20 sections, 10 equations, 15 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Hardware Design
  3. MPC-SEA Design
  4. Finger and Thumb Design
  5. Electronics Integration
  6. Modeling and Characterization
  7. Finger Modeling
  8. Tension Profile Characterization
  9. Grasping Strategy and Algorithm for State Estimation
  10. Experimental Validations
  11. MPC-SEA Experiment
  12. Finger Actuation Experiment
  13. Free-Flexion Case
  14. Object Contact Case
  15. Grasp Functionality Demonstration
  16. ...and 5 more sections

Figures (15)

  • Figure 1: Overview of the proposed anthropomorphic underactuated hand with tendon-based proprioception embedding through PSF modules.
  • Figure 2: Design of MPC-SEA. (a) Appearance and key specifications. (b) Structure and components. (c) Actuation mechanism.
  • Figure 3: Design of the finger and the thumb. (a) Finger (2nd--5th). (b) Thumb.
  • Figure 4: Electronic system architecture of the proposed hand.
  • Figure 5: Relationship between tendon length displacement and joint angles. (a) PIP joint. (b) MCP joint.
  • ...and 10 more figures