{"id":329,"date":"2017-02-10T02:07:44","date_gmt":"2017-02-10T00:07:44","guid":{"rendered":"http:\/\/members.loria.fr\/SIvaldi\/?page_id=329"},"modified":"2022-06-23T01:57:35","modified_gmt":"2022-06-22T23:57:35","slug":"research","status":"publish","type":"page","link":"https:\/\/members.loria.fr\/SIvaldi\/research\/","title":{"rendered":"Research"},"content":{"rendered":"

My research is at the intersection of robotics, learning, control and interaction. I am more and more interested into developing collaborative robots<\/strong>, that are able to interact physically and socially with humans, especially those that are not experts in robotics. I am currently interested in two forms of collaboration: the human-humanoid collaboration, in the form of teleoperation; and the human-cobot or human-exoskeleton physical collaboration. To collaborate, the robot needs a very smart human-aware whole-body control: Human-aware here means that it has to consider the human status, its intent, and its optimization criteria. The robot needs good models of human behavior, and that is why I am interested into human-robot interaction.<\/p>\n

My goal is to “close the loop” on the human, taking into account their feedback into the robot learning and control process. I am also interested in questions related to the “social” impact of collaborative robotics technologies on humans, for example the robot acceptance and trust.<\/p>\n

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Tele-operation of humanoid robots<\/h3>\n

This research started during the project AnDy, where we were facing the problem of teaching collaborative whole-body behaviors to the iCub. In short, whole-body teleoperation is the whole-body version of kinesthetic teaching for learning from demonstration. We developed the tele-operation of the iCub robot, showing that it is possible to replicate the human operator’s movements even if the two systems have different dynamics and the operator could even make the robot fall (Humanoids 2018). We also optimized the humanoid’s whole-body movements, demonstrated by the human, for the robot’s dynamics (Humanoids 2019). Later, we proposed a multimode teleoperation framework that enabled an immersive experience where the operator was watching the robot’s visual feedback inside a VR headset (RAM 2019). To improve the precision in tracking the human’s desired trajectory, we proposed to optimize the whole-body controller’s parameters with the purpose of being “generic” (RA-L 2020). Finally, we focused on the problem of tele-operating the robot in presence of large delays (up to 2 seconds), proposing the technique “prescient teleoperation” that consists in anticipating the human operator using human motion prediction models (arXiv 2021 – under review). We have demonstrated our teleoperation suite on two humanoid robots: iCub and Talos.<\/p>\n

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Exoskeletons<\/h3>\n

This line of research started during the project AnDy. We collaborated with the consortium and particularly with Ottobock gmbh to validate their passive exoskeleton Paexo for overhead works (TNSRE 2019). Later, during covid-19 pandemic, we used our skills with assistive exoskeletons to help the physicians of the University Hospital of Nancy, who used a passive exoskeleton for back assistance during the prone positioning maneuvers of covid patients in the ICU (project ExoTurn, AHFE 2020). We are currently studying active upperbody exoskeletons in the project ASMOA.<\/p>\n

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Machine learning to improve whole-body control<\/h3>\n\n\n\n\n
\"\"<\/a>This line of research started during the project CoDyCo<\/a>, where we explored how to use machine learning techniques to improve the whole-body control of iCub, and the control of its contacts. We learned contact models to improve the inverse dynamics in ICRA 2015<\/a>, then learned how to improve torque control in presence of contacts in HUMANOIDS 2015<\/a>. We didn’t scale to the full humanoid though. At the same time, to enable balancing on our humanoid we were developing whole-body controllers based on multi-task QP controllers, which have the known problem of requiring tedious expert tuning of task priorities and trajectories. So we started to explore how to automatically learn optimal weights and trajectories for whole-body controllers: in ICRA 2016<\/a> we showed that it was possible to learn the evolution of the task weights in time for complex motions of redundant manipulators; in HUMANOIDS 2016<\/a> we scaled the problem to the humanoid case,\u00a0 while ensuring that the optimized weights were “safe”, i.e., never violating any of the problem constraints – and for this we benchmarked different constrained optimization algorithms; in HUMANOIDS 2017<\/a> we applied our learning method to optimize task trajectories rather than task priorities, as in many multi-task QP controllers the task priorities are fixed but the trajectories still need to be optimized, and they can be critical as in some challenging balancing problems.
\nIn the project ANDY<\/a>, I am pushing this line of research so as to realize robust and safe whole-body control, which we need for human-robot collaboration. For example, in HUMANOIDS 2017<\/a> we used trail-and-error algorithms to adapt in few trials on the real robot a QP controller that was good for the simulated robot, but not for the real robot (reality gap problem).<\/td>\n<\/tr>\n
Related topics:<\/p>\n