Optimizing locomotion controllers using biologically-based actuators and objectives

Wang, Jack M.; Hamner, Samuel R.; Delp, Scott L.; Koltun, Vladlen

doi:10.1145/2185520.2185521

Cited by 224 publications

(235 citation statements)

References 57 publications

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“…While inference tasks are easy in this model, learning relies on approximations like contrastive divergence [16]. Models inspired by physics and biology include [30] and [31].…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Efficient Nonlinear Markov Models for Human Motion

Lehrmann

Gehler

Nowozin

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

160

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Dynamic Bayesian networks such as Hidden Markov Models (HMMs) are successfully used as probabilistic models for human motion. The use of hidden variables makes them expressive models, but inference is only approximate and requires procedures such as particle filters or Markov chain Monte Carlo methods. In this work we propose to instead use simple Markov models that only model observed quantities. We retain a highly expressive dynamic model by using interactions that are nonlinear and non-parametric. A presentation of our approach in terms of latent variables shows logarithmic growth for the computation of exact loglikelihoods in the number of latent states. We validate our model on human motion capture data and demonstrate state-of-the-art performance on action recognition and motion completion tasks.

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“…While inference tasks are easy in this model, learning relies on approximations like contrastive divergence [16]. Models inspired by physics and biology include [30] and [31].…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Efficient Nonlinear Markov Models for Human Motion

Lehrmann

Gehler

Nowozin

2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

160

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“…In robotics, there are also many works studied that learn motor primitives for more complex behaviors, such as playing table tennis ( [37], [38], [39]). Some approaches that have been proposed presents systems that control the switches between the physics-based controllers to carry out locomotion ( [34] by optimizing control strategies ( [40], [41]). Among them, the method in [42] presents a muscle-based control for locomotion of bipedal creatures.…”

Section: Learning-based Approachesmentioning

confidence: 99%

Skill learning based catching motion control

Cimen

Kavafoglu

et al. 2015

Computer Animation & Virtual

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In real world, it is crucial to learn biomechanical strategies that prepare the body in kinematics and kinetics terms during the interception tasks, such as kicking, throwing and catching. Based on this, we presents a real-time physics-based approach that generate natural and physically plausible motions for a highly complex task-ball catching. We showed that ball catching behavior as many other complex tasks, can be achieved with the proper combination of rather simple motor skills, such as standing, walking, reaching. Since learned biomechanical strategies can increase the conscious in motor control, we concerned several issues that needs to be planned. Among them, we intensively focus on the concept of timing. The character learns some policies to know how and when to react by using reinforcement learning in order to use time accurately. We demonstrate the effectiveness of our method by presenting some of the catching animation results executed in different catching strategies.In each simulation, the balls were projected randomly, but within a interval of limits, in order to obtain different arrival flight time and height conditions.

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“…In one of the latest work to date, Wang et al [66] propose a biologically-motivated locomotion controller. Their lower-body model is actuated by sixteen Hill's type MTUs (see Sect.…”

Section: Rigid Body Physics and Muscular Actuationmentioning

confidence: 99%