Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics

Kobayashi, Taisuke; Murata, Shingo; Inamura, Tetsunari

doi:10.48550/arxiv.2106.08531

Cited by 1 publication

(2 citation statements)

References 29 publications

(39 reference statements)

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“…Note that, in the original implementation [27], the decoder is also depending on h s , but that is omitted in the above derivation for simplicity and for aggregating time information to z, as well as the literature [31]. In addition, the strength of regularization by the KL term can be controlled by following β-VAE [32] with a hyperparameter β ≥ 0.…”

Section: Variational Recurrent Neural Networkmentioning

confidence: 99%

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Optimization algorithm for feedback and feedforward policies towards robot control robust to sensing failures

Kobayashi

Yoshizawa

2022

Robomech J

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Background and problem statement Model-free or learning-based control, in particular, reinforcement learning (RL), is expected to be applied for complex robotic tasks. Traditional RL requires that a policy to be optimized is state-dependent, that means, the policy is a kind of feedback (FB) controllers. Due to the necessity of correct state observation in such a FB controller, it is sensitive to sensing failures. To alleviate this drawback of the FB controllers, feedback error learning integrates one of them with a feedforward (FF) controller. RL can be improved by dealing with the FB/FF policies, but to the best of our knowledge, a methodology for learning them in a unified manner has not been developed. Contribution In this paper, we propose a new optimization problem for optimizing both the FB/FF policies simultaneously. Inspired by control as inference, the proposed optimization problem considers minimization/maximization of divergences between trajectories, one is predicted by the composed policy and a stochastic dynamics model, and others are inferred as optimal/non-optimal ones. By approximating the stochastic dynamics model using variational method, we naturally derive a regularization between the FB/FF policies. In numerical simulations and a robot experiment, we verified that the proposed method can stably optimize the composed policy even with the different learning law from the traditional RL. In addition, we demonstrated that the FF policy is robust to the sensing failures and can hold the optimal motion.

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Section: Variational Recurrent Neural Networkmentioning

confidence: 99%

“…the stochastic dynamics model, is included and it should be modeled. Indeed, inspired by the literature [31], we found that the model based on the VRNN [27] shown in Eq. ( 10) can naturally yield an additional regularization between the FB/FF policies.…”

Section: Stochastic Dynamics Model With Variational Lower Boundmentioning

confidence: 99%