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A Differentiable Extended Kalman Filter for Object Tracking Under Sliding Regime.

Nicola A Piga1,2, Ugo Pattacini3, Lorenzo Natale1

  • 1Humanoid Sensing and Perception, Istituto Italiano di Tecnologia, Genoa, Italy.

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Summary

This study introduces a novel differentiable Extended Kalman filter for robots to track object position and velocity using only tactile sensing. This enables robots to better handle slipping objects during manipulation tasks.

Keywords:
differentiable extended kalman filteringhumanoid roboticsmachine learning-aided filteringobject position trackingobject velocity tracking

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Tactile sensing provides crucial information for robotic manipulation, enabling perception of object properties and state.
  • Current robotic systems often struggle with tracking objects during dynamic motions like slipping, limiting manipulation capabilities.
  • Existing object pose estimation algorithms typically assume static contact, neglecting the complexities of sliding and slipping.

Purpose of the Study:

  • To develop a novel algorithm for tracking object pose and velocity during translational sliding using tactile data.
  • To enable robots to perceive and react to object slippage in real-time.
  • To improve the robustness of robotic manipulation systems in the presence of object motion.

Main Methods:

  • A differentiable Extended Kalman filter was designed and trained for tactile-based object state tracking.
  • The filter processes tactile observations to estimate object position and velocity.
  • Experiments were conducted on the iCub humanoid robot platform with various objects.

Main Results:

  • The proposed approach achieved an average position tracking error of approximately 0.6 cm.
  • The system successfully tracked the object's state (position and velocity) during translational sliding.
  • The estimated object state was sufficient for making informed control decisions.

Conclusions:

  • The differentiable Extended Kalman filter effectively tracks object pose and velocity from tactile input alone.
  • This method enhances robotic manipulation by enabling real-time adaptation to object slippage.
  • The findings suggest a pathway towards more sophisticated tactile-based robotic control and perception.