Landmark-Based Goal Recognition for Shared Autonomy: A Framework for Enhanced Teleoperation

Abstract

Shared autonomy is the future of teleoperation as it reduces the teleoperator’s burden, enhances capabilities, and improves embodiment by offering seamless control of the robot. However, it remains rarely used, particularly with humanoid robots, as it faces numerous challenges. In this work, we introduce an innovative shared autonomy framework suitable for a wide range of robots, which we tested on a humanoid robot. This framework leverages Bayesian filtering over a Hidden Markov Model (HMM) to perform goal recognition, employing a landmark-based heuristic that minimizes computational demands while computing observation likelihoods without prior knowledge or a cost function. Once the teleoperator’s goal is identified, the robot assists according to its confidence level in the goal prediction. Assistance is provided by guiding the robot’s end-effector to reach a specified target position and orientation. In experiments with a diverse group of 10 teleoperators, conducted with video transmission delay, we achieved high accuracy in goal prediction and demonstrated significantly faster teleoperation time with shared autonomy.