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therapy into entertaining, game like actions, and (3) embed therapy within patient-centered life
like practical activities
Human Robot Interaction:
Realistic human robot interaction was achieved through an effective and robust tracker
for humanoid head, LILLY via visual servoing to track people at ARRI’s humanoid lab [47]. The
visual servo routines for tracking were optimized though minimization of a cost function for the
servo controller is presented in [47]. Also inclusion of the learning phase for the control
algorithm was designed through a reward function in TD reinforcement learning approach,
showed that the tracking accuracy was greatly improved for the humanoid head [54].
A tracking system for an object through pose prediction via Extend Kalman Filters and
visual feedback control algorithms was presented in [56]. The servo controllers were tuned
through Ziegler-Nichols PID tuning methods and tracking algorithms were tested on a PTZ
camera. Also this paper proposed an optimization approach for realistic motion in robot with
real-time vision based feedback control. Results presented showed that tracking through the
neck and eye motions for the robot actor resulting from this scheme was realistic in comparison
to that of humans [55] [54].
Reinforcement learning to improve interactivity and effectiveness in the human robot
interaction was discussed in [43]. Adaptive mapping algorithms for interface devices were
employed with learning based on reward function from the user. A model for updating the
interface mapping was proposed where the policy of interface mapping, value functions and
state property weights were updated based on the metric evaluation of the actions was
performed. [43] shows robustness of the algorithm in mapping interfaces which provided a
technique where the robot interactive actions were not only based on the user input but also
learning from the results of the action performed. The algorithm showed increased accuracy
and robustness as the number of trails conducted increased [54].
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