This paper presents a novel shared autonomy and baseline policy adapting framework for human-robot interactions in high-level context-aware robotic tasks. With a unique methodology that leverages hierarchies in decision-making as well as variational analysis of human policy, we propose a mathematical model of shared autonomy policy. The framework aims at interpretable high-level decision-making for efficient robot operation with human in the loop. We modeled the decision-making process using hierarchical Markov decision processes (MDPs) in an algorithm we calledpolicy adapting, where the autonomous system policy is adapted, and hence, shaped by incorporating design variables contextual to the robot, human, task, and pre-training. By integrating deep reinforcement learning within a multi-agent hierarchical context, we present an end-to-end algorithm to train a baseline policy designed for shared autonomy. We showcase the effectiveness of our framework, and particularly the interplay between different design elements and human's skill level, in a pilot study with a human user in a simulated sequence of high-level pick-and-place tasks. The proposed framework advances the state-of-the-art in shared autonomy for robotic tasks, but can also be applied to other domains of autonomous operation.

Operating an articulated machine is a complex and hierarchical task, involving several levels of decision making. Motivated by the timber-harvesting applications of these machines, we are interested in developing a collaborative framework for operating an articulated machine/robot in order to increase its level of autonomy. In this paper, we consider two problems in the context of collaborative operation of a feller-buncher: first, the problem of planning a sequence of cut/grasp/bunch tasks for the trees in the vicinity of the machine. Here we propose a human-inspired planning algorithm based on our observations of the operators in the field. Then, a Markov Decision Process (MDP) framework is provided, which enables us to obtain an optimal sequence of tasks. We provide numerical illustrations of how our MDP framework works. Second is the problem of inferring the operator's goal from the motions of the machine. The goal inference algorithm presented here enables the robot equipped with the planning intelligence to perceive the human's intent in real-time. We evaluate the performance of our goal inference algorithm through a user-study with a feller-buncher simulator. The results show the benefits of our algorithm over a robot that assumes the human is moving to the closest target.