2,165 research outputs found
Morphology independent dynamic locomotion control for virtual characters
Physically based animation of virtual characters is an attractive technology for computer games. It enables characters to dynamically react to interactions with the environment. Existing dynamic simulation controllers are often complex to understand and manipulate, and so are of limited use for animators. This paper presents an extended spline-based control strategy similar to splines used in standard keyframe animation techniques. Unlike existing dynamic control strategies, this allows animators to modify the control system parameters in a manner similar to traditional kinematic animation techniques. A genetic algorithm is employed to produce the initial control parameters for the desired gait, and extend the parameters to enable sensory feedback. The controllers are simulated in a 3D environment and demonstrated for bipedal, tripedal and snake-like characters
Training Physics-based Controllers for Articulated Characters with Deep Reinforcement Learning
In this thesis, two different applications are discussed for using machine learning techniques to train coordinated motion controllers in arbitrary characters in absence of motion capture data. The methods highlight the resourcefulness of physical simulations to generate synthetic and generic motion data that can be used to learn various targeted skills. First, we present an unsupervised method for learning loco-motion skills in virtual characters from a low dimensional latent space which captures the coordination between multiple joints. We use a technique called motor babble, wherein a character interacts with its environment by actuating its joints through uncoordinated, low-level (motor) excitation, resulting in a corpus of motion data from which a manifold latent space can be extracted. Using reinforcement learning, we then train the character to learn locomotion (such as walking or running) in the low-dimensional latent space instead of the full-dimensional joint action space. The thesis also presents an end-to-end automated framework for training physics-based characters to rhythmically dance to user-input songs. A generative adversarial network (GAN) architecture is proposed that learns to generate physically stable dance moves through repeated interactions with the environment. These moves are then used to construct a dance network that can be used for choreography. Using DRL, the character is then trained to perform these moves, without losing balance and rhythm, in the presence of physical forces such as gravity and friction
Populating 3D Cities: a True Challenge
In this paper, we describe how we can model crowds in real-time using dynamic meshes, static meshes andimpostors. Techniques to introduce variety in crowds including colors, shapes, textures, individualanimation, individualized path-planning, simple and complex accessories are explained. We also present ahybrid architecture to handle the path planning of thousands of pedestrians in real time, while ensuringdynamic collision avoidance. Several behavioral aspects are presented as gaze control, group behaviour, aswell as the specific technique of crowd patches
Populating 3D Cities: A True Challenge
In this paper, we describe how we can model crowds in real-time using dynamic meshes, static meshes andimpostors. Techniques to introduce variety in crowds including colors, shapes, textures, individualanimation, individualized path-planning, simple and complex accessories are explained. We also present ahybrid architecture to handle the path planning of thousands of pedestrians in real time, while ensuringdynamic collision avoidance. Several behavioral aspects are presented as gaze control, group behaviour, aswell as the specific technique of crowd patches
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
The physical design of a robot and the policy that controls its motion are
inherently coupled, and should be determined according to the task and
environment. In an increasing number of applications, data-driven and
learning-based approaches, such as deep reinforcement learning, have proven
effective at designing control policies. For most tasks, the only way to
evaluate a physical design with respect to such control policies is
empirical--i.e., by picking a design and training a control policy for it.
Since training these policies is time-consuming, it is computationally
infeasible to train separate policies for all possible designs as a means to
identify the best one. In this work, we address this limitation by introducing
a method that performs simultaneous joint optimization of the physical design
and control network. Our approach maintains a distribution over designs and
uses reinforcement learning to optimize a control policy to maximize expected
reward over the design distribution. We give the controller access to design
parameters to allow it to tailor its policy to each design in the distribution.
Throughout training, we shift the distribution towards higher-performing
designs, eventually converging to a design and control policy that are jointly
optimal. We evaluate our approach in the context of legged locomotion, and
demonstrate that it discovers novel designs and walking gaits, outperforming
baselines in both performance and efficiency
Quantifying Morphological Computation
The field of embodied intelligence emphasises the importance of the
morphology and environment with respect to the behaviour of a cognitive system.
The contribution of the morphology to the behaviour, commonly known as
morphological computation, is well-recognised in this community. We believe
that the field would benefit from a formalisation of this concept as we would
like to ask how much the morphology and the environment contribute to an
embodied agent's behaviour, or how an embodied agent can maximise the
exploitation of its morphology within its environment. In this work we derive
two concepts of measuring morphological computation, and we discuss their
relation to the Information Bottleneck Method. The first concepts asks how much
the world contributes to the overall behaviour and the second concept asks how
much the agent's action contributes to a behaviour. Various measures are
derived from the concepts and validated in two experiments which highlight
their strengths and weaknesses
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