Vector Quantization for Deep-Learning-Based CSI Feedback in Massive MIMO Systems

This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework while reducing its computational complexity based on shape-gain vector quantization. In this method, the magnitude of the latent vector is quantized using a non-uniform scalar codebook with a proper transformation function, while the direction of the latent vector is quantized using a trainable Grassmannian codebook. A multi-rate codebook design strategy is also developed by introducing a codeword selection rule for a nested codebook along with the design of a loss function. Simulation results demonstrate that the proposed method reduces the computational complexity associated with VQ-VAE while improving CSI reconstruction performance under a given feedback overhead

A Hybrid Approach to Multiple Fluid Simulation using Volume Fractions

This paper presents a hybrid approach to multiple fluid simulation that can handle miscible and immiscible fluids, simultaneously. We combine distance functions and volume fractions to capture not only the discontinuous interface between immiscible fluids but also the smooth transition between miscible fluids. Our approach consists of four steps: velocity field computation, volume fraction advection, miscible fluid diffusion, and visualization. By providing a combining scheme between volume fractions and level set functions, we are able to take advantages of both representation schemes of fluids. From the system point of view, our work is the first approach to Eulerian grid-based multiple fluid simulation including both miscible and immiscible fluids. From the technical point of view, our approach addresses the issues arising from variable density and viscosity together with material diffusion. We show that the effectiveness of our approach to handle multiple miscible and immiscible fluids through experiments.We would like to thank Jihyeon Yi and Donghoon Sagong for their help. This work was supported by the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MEST) (No. 2009-0058607)

Model Predictive Control with a Visuomotor System for Physics-based Character Animation

This article presents a Model Predictive Control framework with a visuomotor system that synthesizes eye and head movements coupled with physics-based full-body motions while placing visual attention on objects of importance in the environment. As the engine of this framework, we propose a visuomotor system based on human visual perception and full-body dynamics with contacts. Relying on partial observations with uncertainty from a simulated visual sensor, an optimal control problem for this system leads to a Partially Observable Markov Decision Process, which is difficult to deal with. We approximate it as a deterministic belief Markov Decision Process for effective control. To obtain a solution for the problem efficiently, we adopt differential dynamic programming, which is a powerful scheme to find a locally optimal control policy for nonlinear system dynamics. Guided by a reference skeletal motion without any a priori gaze information, our system produces realistic eye and head movements together with full-body motions for various tasks such as catching a thrown ball, walking on stepping stones, balancing after being pushed, and avoiding moving obstacles. </jats:p

A Hybrid Approach to Multiple Fluid Simulation using Volume Fractions

This paper presents a hybrid approach to multiple fluid simulation that can handle miscible and immiscible fluids, simultaneously. We combine distance functions and volume fractions to capture not only the discontinuous interface between immiscible fluids but also the smooth transition between miscible fluids. Our approach consists of four steps: velocity field computation, volume fraction advection, miscible fluid diffusion, and visualization. By providing a combining scheme between volume fractions and level set functions, we are able to take advantages of both representation schemes of fluids. From the system point of view, our work is the first approach to Eulerian grid-based multiple fluid simulation including both miscible and immiscible fluids. From the technical point of view, our approach addresses the issues arising from variable density and viscosity together with material diffusion. We show that the effectiveness of our approach to handle multiple miscible and immiscible fluids through experiments.Computer Graphics Forum29

On-line Real-time Physics-based Predictive Motion Control with Balance Recovery

In this paper, we present an on-line real-time physics-based approach to motion control with contact repositioning based on a low-dimensional dynamics model using example motion data. Our approach first generates a reference motion in run time according to an on-line user request by transforming an example motion extracted from a motion library. Guided by the reference motion, it repeatedly generates an optimal control policy for a small time window one at a time for a sequence of partially overlapping windows, each covering a couple of footsteps of the reference motion, which supports an on-line performance. On top of this, our system dynamics and problem formulation allow to derive closed-form derivative functions by exploiting the low-dimensional dynamics model together with example motion data. These derivative functions and their sparse structures facilitate a real-time performance. Our approach also allows contact foot repositioning so as to robustly respond to an external perturbation or an environmental change as well as to perform locomotion tasks such as stepping on stones effectively.Computer Graphics Foru

Physics-based full-body soccer motion control for dribbling and shooting

Playing with a soccer ball is not easy even for a real human because of dynamic foot contacts with the moving ball while chasing and controlling it. The problem of online full-body soccer motion synthesis is challenging and has not been fully solved yet. In this paper, we present a novel motion control system that produces physically-correct full-body soccer motions: dribbling forward, dribbling to the side, and shooting, in response to an online user motion prescription specified by a motion type, a running speed, and a turning angle. This system performs two tightly-coupled tasks: data-driven motion prediction and physics-based motion synthesis. Given example motion data, the former synthesizes a reference motion in accordance with an online user input and further refines the motion to make the character kick the ball at a right time and place. Provided with the reference motion, the latter then adopts a Model Predictive Control (MPC) framework to generate a physically-correct soccer motion, by solving an optimal control problem that is formulated based on dynamics for a full-body character and the moving ball together with their interactions. Our demonstration shows the effectiveness of the proposed system that synthesizes convincing full-body soccer motions in various scenarios such as adjusting the desired running speed of the character, changing the velocity and the mass of the ball, and maintaining balance against external forces.</jats:p