Intent-Informed Model Predictive Control for Shared Autonomy in Remote Manipulation Tasks

Key points

• In this paper, we propose a novel arbitration strategy that dynamically adjusts the level of autonomy in the system using the entropy of a belief distribution derived from a multimodal intent inference.1
• Human intent is modeled with a Dynamic Bayesian Network (DBN), which updates beliefs over goals given real-time operator gaze data and robot arm motion during teleoperation.1
• We then formulate the blending of human input and autonomous control as a Model Predictive Control (MPC) problem, where the system's autonomous behavior is guided by the belief distribution.1
• To validate our approach, we conducted experiments to compare the performance of our proposed algorithm against two MPC-based shared autonomy methods.1

Sources (1)

• [1]Intent-Informed Model Predictive Control for Shared Autonomy in Remote Manipulation Tasks
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 05:59 PM

  Shared autonomy is a control paradigm that combines human cognitive abilities with robot precision to enable effective remote manipulation in complex environments.

  In this paper, we propose a novel arbitration strategy that dynamically adjusts the level of autonomy in the system using the entropy of a belief distribution derived from a multimodal intent inference.

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