Effectiveness of Kinesthetic Sensing in In-Hand Rotation of Objects with an Eccentric Center of Mass

Chanyoung Ahn, Sungwoo Park, Donghyun Hwang

Keywords: In-Hand Manipulation, Reinforcement Learning, Force and Tactile Sensing

TL;DR: We explore how kinesthetic feedback, such as joint forces and torques, helps a robotic hand adapt to varying object properties. With PCA and RL, it improves performance by up to 2.40 times in unseen objects.

Abstract: In-hand manipulation is a key capability for dexterous control, yet it becomes challenging when the mass or center of mass (CoM) of an object is not well known. Such intrinsic properties are difficult to infer precisely through visual sensing alone, which limits the reliability of manipulation strategies. This study investigates how kinesthetic sensing can support in-hand rotational tasks by enabling reinforcement learning (RL) agents to adjust to variations in object dynamics, particularly weight and center of mass. Our method incorporates both proprioceptive signals, such as joint angles, and kinesthetic data, joint forces and torques, captured from sensors embedded in a four-finger robotic hand. To reduce the dimensionality of the input space while retaining the relevant dynamics, we applied Principal Component Analysis (PCA). The resulting policy demonstrates improved adaptability. In the simulation, the manipulation success rates increased by 2.09 and 2.40 times on six and twelve previously unseen CoM configurations, respectively. In addition, kinesthetic detection improves performance 1.52 times in ten known configurations. These findings indicate that kinesthetic feedback contributes substantially to robust and generalizable in-hand manipulation. To access our data and video, please visit our project page: https://cold-young.github.io/kinesthetic_rotation/.

Submission Number: 15