Intent-aware control in kinematically redundant systems: Towards collaborative wearable robots (bibtex)
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Bibtex Entry:
  author    = {Khoramshahi, M and Morel, G and Jarrassé, N},
  title     = {{Intent-aware control in kinematically redundant systems: Towards collaborative wearable robots}},
  booktitle = {{2021 IEEE International Conference on Robotics and Automation (ICRA)}},
  year      = {2021},
  pages     = {xx},
  address   = {Xi'an, China},
  month     = May,
  abstract  = {Many human-robot collaboration scenarios can be seen as a redundant leader-follower setup where the human (i.e., the leader) can potentially perform the task without the assistance of the robot (i.e., the follower). Thus, the goal of the collaboration, beside stable execution of the task, is to reduce the human cost; e.g., ergonomic, or cognitive cost. Such system redundancies (where the same task be achieved in different manner) can also be exploited as a communication channel for the human to convey his/her intention to the robot; since it is essential for the overall performance (both execution and assistance) that the follower recognizes the intended task in an online fashion. Having an estimation for the intended task, the robot can assist the human by reducing the human cost over the task null-space; i.e., the null-space which arises from the overall system redundancies with respect to the intended task. With the prospective of supernumerary and prosthetic robots, in this work, we primarily focus on serial manipulation in which the proximal/distal part of the kinematic chain is controlled by the leader/follower respectively. By exploiting kinematic redundancies for intention-recognition and costminimization, our proposed control strategy (for the follower) ensures assistance under stable execution of the task. Our results (simulations and preliminary experimentation) show the efficacy of our method in providing a seamless robotic assistance (i.e., improving human posture) toward human intended tasks (i.e., reaching motions) for wearable robotics.},
  category  = {ACTIS},
  crac      = {n},
  hal       = {y},
  pdf       = {},
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