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.

Trying to infer the strategy of the opponent is very important in games. Especially in Real-Time Strategy Games (RTS), where you have uncertainty and thus cannot see most of the opponent’s actions, but you do not want to be unprepared for its strategy. Good human players can do it almost naturally, but it is a different story for AI players. Plan recognition is a challenging problem, especially with uncertainty and the number of possible actions and states of the world in RTS games. We address the problem of plan recognition in RTS games. We show that an approach based on plan recognition as planning and heuristic search can yield good results and be robust to noise. Furthermore, we found that such an approach has its accuracy decreasing slightly with noisy data, does not need any training beforehand, and could be easily adapted to different RTS games.Our approach allows us to infer in real-time the plan that a player might be pursuing in an RTS game, here we focus on the RTS game called StarCraft, but it could be adapted to many other RTS games.

Digital games have proven to be valuable simulation environments for plan and goal recognition. Though, goal recognition is a hard problem, especially in the field of digital games where players unintentionally achieve goals through exploratory actions, abandon goals with little warning, or adopt new goals based upon recent or prior events. In this paper, a method using simulation and bayesian programming to infer the player's strategy in a Real-Time-Strategy game (RTS) is described, as well as how we could use it to make more adaptive AI for this kind of game and thus make more challenging and entertaining games for the players.