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Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot.

Tadahiro Taniguchi1, Ryo Yoshino1, Toshiaki Takano2

  • 1Emergent Systems Laboratory, College of Information Science and Engineering, Ritsumeikan University, Ksatsu Japan.

Frontiers in Neurorobotics
|June 7, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an active perception method for robots to recognize objects using multimodal hierarchical Dirichlet process (MHDP). The approach efficiently selects actions to maximize information gain, enabling faster and more accurate object recognition.

Keywords:
active perceptioncognitive roboticsmultimodal machine learningsubmodular maximizationtopic model

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Robots can form object categories using multimodal information (visual, auditory, haptic) through actions.
  • Real-time object recognition requires efficient action selection due to time constraints.

Purpose of the Study:

  • To propose an active perception method for robots to recognize object categories efficiently.
  • To address the challenge of selecting the most effective actions for object recognition in limited time.

Main Methods:

  • Utilized the multimodal hierarchical Dirichlet process (MHDP) for category formation.
  • Employed an information gain (IG) maximization criterion with a lazy greedy algorithm for action selection.
  • Derived a Monte Carlo approximation for IG calculation, leveraging MHDP properties.
  • Demonstrated that IG maximization is a submodular problem, suitable for greedy algorithms.

Main Results:

  • The proposed method enables robots to select actions that lead to quick and accurate object recognition.
  • Experiments with a humanoid robot and synthetic data validated the method's effectiveness.
  • The approach performs well even with a large number of actions and multi-class object sets.

Conclusions:

  • The active perception method based on MHDP and IG maximization is effective for real-time object recognition.
  • The theoretical framework supports the use of greedy algorithms for optimizing action selection.
  • The method offers a practical solution for robots needing to identify objects efficiently in dynamic environments.