@article {pub3965,
	title = {Partner Adaptive Dyadic collaborative Manipulation through Informed Hybrid Bilevel Optimization},
	author = {Theodoros	 Stouraitis AND Michael Gienger AND Sethu Vijayakumar},
	year = {2020},
	month = {August},
	abstract = {Effective dyadic collaboration is based on individ-
uals{\textquoteright} ability to adapt their policy to their partner{\textquoteright}s actions.
This article provides a principled formalism to address online
adaptation in joint planning problems such as Dyadic col-
laborative Manipulation (DcM) scenarios. Human{\textquoteright}s intentions
are represented as task space goals and are realised as task
space forces. We propose a computational bilevel formulation to
solve the joint non-stationary problem holistically by integrating
informed search algorithms with hybrid trajectory optimization.
The proposed method is the first to empower robotic agents
with the ability to plan online in hybrid spaces {\textendash} optimizing
over discrete contact locations, and arms coordination, contin-
uous trajectory, and force profiles, for co-manipulation tasks
with switching dyadic objective goals. The task of finding the
appropriate contact points, arm coordination pattern, forces and
the respective timing of grasp-hold changes are carried out by
a informed bilevel optimization using both graph-traversal and
non-linear solvers. We demonstrate the efficacy of the bilevel
optimization method by investigating the effect of robot policy
changes (trajectories, timings, grasp-holds, arms{\textquoteright} coordination)
based on online changes of the partner{\textquoteright}s policy in numerous sim-
ulations. We also realize, in hardware, effective co-manipulation
of a large objects by the human and the robot, including changes
of task goals, eminent grasp switches as well as optimal dyadic
interactions to realize the non-stationary joint task.},
	publisher = {IEEE},
	journal = {IEEE Transactions on Robotics}
}