Humanoid Control

7 papers with code • 0 benchmarks • 0 datasets

Control of a high-dimensional humanoid. This can include skill learning by tracking motion capture clips, learning goal-directed tasks like going towards a moving target, and generating motion within a physics simulator.

Most implemented papers

Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation

marikoisogawa/OpticalNLOSPose CVPR 2020

We bring together a diverse set of technologies from NLOS imaging, human pose estimation and deep reinforcement learning to construct an end-to-end data processing pipeline that converts a raw stream of photon measurements into a full 3D human pose sequence estimate.

Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis

Khrylx/RFC NeurIPS 2020

Our approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3. 6M) and generates diverse long-term motions.

ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution

nnaisense/pgpelib 5 Aug 2020

In these algorithms, gradients of the total reward with respect to the policy parameters are estimated using a population of solutions drawn from a search distribution, and then used for policy optimization with stochastic gradient ascent.

On the model-based stochastic value gradient for continuous reinforcement learning

facebookresearch/svg 28 Aug 2020

For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents.

Learning to Brachiate via Simplified Model Imitation

brachiation-rl/brachiation 8 May 2022

Key to our method is the use of a simplified model, a point mass with a virtual arm, for which we first learn a policy that can brachiate across handhold sequences with a prescribed order.

Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts

ZhaomingXie/soccer_juggle_release SIGGRAPH 2022

Learning physics-based character controllers that can successfully integrate diverse motor skills using a single policy remains a challenging problem.

MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control

microsoft/MoCapAct 15 Aug 2022

We demonstrate the utility of MoCapAct by using it to train a single hierarchical policy capable of tracking the entire MoCap dataset within dm_control and show the learned low-level component can be re-used to efficiently learn downstream high-level tasks.