Sovannara Hak

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—This paper presents a complete methodology to quickly reshape a dynamic motion demonstrated by a human and to adapt the dynamics of the human to the dynamics of the robot. The method uses an inverse dynamics control scheme with a quadratic programming optimization solver. The motion data recorded using a motion capture system is introduced into the control(More)
Efficient methods to perform motion recognition have been developed using statistical tools. Those methods rely on primitive learning in a suitable space, for example, the latent space of the joint angle and/or adequate task spaces. Learned primitives are often sequential: A motion is segmented according to the time axis. When working with a humanoid robot,(More)
— Redundant robots performing multiple tasks of different priority levels are often handled by hierarchical control frameworks. Existing hierarchical controllers can handle either strict task priorities by using null space projections or a sequence of quadratic programs, or non strict task priorities by using a weighting strategy. This paper proposes a(More)
—In October 2012, the humanoid robot HRP-2 was presented during a live demonstration performing fine-balanced dance movements with a human performer in front of more than 1000 people. This success was possible by the systematic use of operational-space inverse dynamics to compute dynamically consistent movements following a motion capture pattern(More)
— We present a method using simple mechanisms to perform a real time imitation on a humanoid robot. Instead of focusing on the fidelity of the motion, we will rather focus on what controllers have to be activated to perform a desired motion. The originality of our approach is to work in the task spaces of the robot in order to solve simultaneously the(More)
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