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Auto-conditioned recurrent networks for extended complex human motion synthesis
Guided learning of control graphs for physics-based characters
The difficulty of develo** control strategies has been a primary bottleneck in the adoption
of physics-based simulations of human motion. We present a method for learning robust …
of physics-based simulations of human motion. We present a method for learning robust …
Online control of simulated humanoids using particle belief propagation
We present a novel, general-purpose Model-Predictive Control (MPC) algorithm that we call
Control Particle Belief Propagation (C-PBP). C-PBP combines multimodal, gradient-free …
Control Particle Belief Propagation (C-PBP). C-PBP combines multimodal, gradient-free …
Dynamic terrain traversal skills using reinforcement learning
The locomotion skills developed for physics-based characters most often target flat terrain.
However, much of their potential lies with the creation of dynamic, momentum-based …
However, much of their potential lies with the creation of dynamic, momentum-based …
Fast and flexible multilegged locomotion using learned centroidal dynamics
We present a flexible and efficient approach for generating multilegged locomotion. Our
model-predictive control (MPC) system efficiently generates terrain-adaptive motions, as …
model-predictive control (MPC) system efficiently generates terrain-adaptive motions, as …
Simulation and control of skeleton-driven soft body characters
In this paper we present a physics-based framework for simulation and control of human-like
skeleton-driven soft body characters. We couple the skeleton dynamics and the soft body …
skeleton-driven soft body characters. We couple the skeleton dynamics and the soft body …