Human-in-the-Loop Mixed-Initiative Control under Temporal Tasks
It addresses the challenge of integrating human initiatives into robot autonomy for motion control and task planning, which is incremental in combining existing methods like LTL and IRL.
This paper tackles the problem of controlling mobile robots under complex temporal tasks while incorporating human input, proposing an online coordination scheme that ensures safety and adapts to new tasks and human preferences, with results demonstrated through simulations and experiments.
This paper considers the motion control and task planning problem of mobile robots under complex high-level tasks and human initiatives. The assigned task is specified as Linear Temporal Logic (LTL) formulas that consist of hard and soft constraints. The human initiative influences the robot autonomy in two explicit ways: with additive terms in the continuous controller and with contingent task assignments. We propose an online coordination scheme that encapsulates (i) a mixed-initiative continuous controller that ensures all-time safety despite of possible human errors, (ii) a plan adaptation scheme that accommodates new features discovered in the workspace and short-term tasks assigned by the operator during run time, and (iii) an iterative inverse reinforcement learning (IRL) algorithm that allows the robot to asymptotically learn the human preference on the parameters during the plan synthesis. The results are demonstrated by both realistic human-in-the-loop simulations and experiments.