  In this work, we propose a novel shared autonomy framework to operate
articulated robots. We provide strategies to design both the task-oriented
hierarchical planning and policy shaping algorithms for efficient human-robot
interactions in context-aware operation of articulated robots. Our framework
for interplay between the human and the autonomy, as the participating agents
in the system, is particularly influenced by the ideas from multi-agent
systems, game theory, and theory of mind for a sliding level of autonomy. We
formulate the sequential hierarchical human-in-the-loop decision making process
by extending MDPs and Options framework to shared autonomy, and make use of
deep RL techniques to train an uncertainty-aware shared autonomy policy. To
fine-tune the formulation to a human, we use history of the system states,
human actions, and their error with respect to a surrogate optimal model to
encode human's internal state embeddings, beyond the designed values, by using
conditional VAEs. We showcase the effectiveness of our formulation for
different human skill levels and degrees of cooperativeness by using a case
study of a feller-buncher machine in the challenging tasks of timber
harvesting. Our framework is successful in providing a sliding level of
autonomy from fully autonomous to fully manual, and is particularly successful
in handling a noisy non-cooperative human agent in the loop. The proposed
framework advances the state-of-the-art in shared autonomy for operating
articulated robots, but can also be applied to other domains where autonomous
operation is the ultimate goal.
