Iterative method of measurement with a given accuracy for angular velocity errors

Bubnov, A. V.; Emashov, V. A.; Chudinov, A. N.

doi:10.1109/sibcon.2015.7147052

Cited by 7 publications

(1 citation statement)

References 7 publications

(15 reference statements)

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“…If a robotic task is in the Markovian environment, the state of the robot agent should include not only the angular position but also the rate of the position difference (angular velocity) to predict the next state based on the current state. However, robots using low-cost motors such as Dynamixel [19] may not provide accurate angular velocity due to sensor errors and delays in the control system [20]. Because our goal is to build a model that can be applied to such low-cost systems, we modeled our motion retargeting as a non-Markovian problem where the state of the agent has only positional information without velocity.…”

Section: Introductionmentioning

confidence: 99%

C-3PO: Cyclic-Three-Phase Optimization for Human-Robot Motion Retargeting based on Reinforcement Learning

Kim

Lee

2019

Preprint

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Motion retargeting between heterogeneous polymorphs with different sizes and kinematic configurations requires a comprehensive knowledge of kinematics and inverse kinematics. Moreover, it is non-trivial to provide a kinematic independent general solution. In this study, we developed a cyclic three-phase optimization method based on deep reinforcement learning for human-robot motion retargeting. The motion retargeting and reward calculations were performed using refined data in a latent space by the cyclic and filtering paths of our method. In addition, the human-in-the-loop based threephase approach provides a framework for the improvement of the motion retargeting policy by both quantitative and qualitative manners. Using the proposed C-3PO method, we were successfully able to learn the motion retargeting skill between the human skeleton and the real NAO, Pepper, Baxter and C-3PO robot motions.

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Section: Introductionmentioning

confidence: 99%