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Reinforcement Learning With Sequences of Motion Primitives for Robust Manipulation

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Momentum control for balance

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Contact-aware nonlinear control of dynamic characters

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Robust physics-based locomotion using low-dimensional planning

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Optimal gait and form for animal locomotion

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Sampling-based contact-rich motion control

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Near-optimal character animation with continuous control

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Optimizing walking controllers for uncertain inputs and environments

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Trajectory Optimization for Full-Body Movements with Complex Contacts

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Guided Learning of Control Graphs for Physics-Based Characters

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Stable Proportional-Derivative Controllers

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Interactive Character Animation Using Simulated Physics: A State‐of‐the‐Art Review

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Optimal feedback control for character animation using an abstract model

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Robust task-based control policies for physics-based characters

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Online control of simulated humanoids using particle belief propagation

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Responsive characters from motion fragments

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Simulation and control of skeleton-driven soft body characters

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Learning bicycle stunts

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Control Systems for Human Running using an Inverted Pendulum Model and a Reference Motion Capture Sequence

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Dynamic terrain traversal skills using reinforcement learning

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Linear Bellman combination for control of character animation

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Improving Sampling‐based Motion Control

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Momentum-Mapped Inverted Pendulum Models for Controlling Dynamic Human Motions

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Active learning for real-time motion controllers

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Controlling physics-based characters using soft contacts

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Biped walking control using a trajectory library

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Control of Rotational Dynamics for Ground and Aerial Behavior

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Feedback control for rotational movements in feature space

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Simulating 2D Gaits with a Phase-Indexed Tracking Controller

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Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

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DeepMimic

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Mode-adaptive neural networks for quadruped motion control

Zhang, H., Starke, S., Komura, T., & Saito, J. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201366 · Cited by 276 · see citing works

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Robust motion in-betweening

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PhysDiff: Physics-Guided Human Motion Diffusion Model

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ASE

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SFV

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DReCon

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Learning symmetric and low-energy locomotion

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Scalable muscle-actuated human simulation and control

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Learning predict-and-simulate policies from unorganized human motion data

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Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning

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A scalable approach to control diverse behaviors for physically simulated characters

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The Path Planning of Mobile Robot by Neural Networks and Hierarchical Reinforcement Learning

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SimPoE: Simulated Character Control for 3D Human Pose Estimation

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Ego-Pose Estimation and Forecasting As Real-Time PD Control

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ALLSTEPS: Curriculum‐driven Learning of Stepping Stone Skills

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Interactive character animation by learning multi-objective control

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Physics-based character controllers using conditional VAEs

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MCP: Learning Composable Hierarchical Control with Multiplicative Compositional PoliciesPreprint

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Learning to dress: Synthesizing human dressing motion via deep reinforcement learning

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A Survey on Deep Learning for Skeleton‐Based Human Animation

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Learning Fast Adaptation With Meta Strategy Optimization

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ControlVAE

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Sharp interface approaches and deep learning techniques for multiphase flows

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Recurrent transition networks for character locomotion

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Hierarchical visuomotor control of humanoidsPreprint

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The Role of Physics-Based Simulators in Robotics

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Control strategies for physically simulated characters performing two-player competitive sports

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Rhythm is a Dancer: Music-Driven Motion Synthesis With Global Structure

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Deep motifs and motion signatures

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Efficient Neural Networks for Real-time Motion Style Transfer

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CARL

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SuperTrack

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Deep learning of biomimetic sensorimotor control for biomechanical human animation

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Fluid directed rigid body control using deep reinforcement learning

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

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NNWarp: Neural Network-based Nonlinear Deformation

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DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment

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GREIL-Crowds: Crowd Simulation with Deep Reinforcement Learning and Examples

Charalambous, P., Pettré, J., Vassiliades, V., Chrysanthou, Y., & Pelechano, N. (2023) · ACM Transactions on Graphics · doi:10.1145/3592459 · Cited by 48 · see citing works

MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations

Yao, H., Song, Z., Zhou, Y., Ao, T., Chen, B., & Liu, L. (2024) · ACM Transactions on Graphics · doi:10.1145/3658137 · Cited by 46 · see citing works

Mobility-aware load Balancing for Reliable Self-Organization Networks: Multi-agent Deep Reinforcement Learning

Mohajer, A., Bavaghar, M., & Farrokhi, H. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107056 · Cited by 45 · see citing works

Generative Tweening: Long-term Inbetweening of 3D Human MotionsPreprint

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UniCon: Universal Neural Controller For Physics-based Character MotionPreprint

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A Comparison of Action Spaces for Learning Manipulation Tasks

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Learning time-critical responses for interactive character control

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Aerobatics control of flying creatures via self-regulated learning

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Reinforcement Learning with Competitive Ensembles of Information-Constrained PrimitivesPreprint

Goyal, A., Sodhani, S., Binas, J., Peng, X. B., Levine, S., & Bengio, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10667 · Cited by 30 · see citing works

Positioning of the Robotic Arm Using Different Reinforcement Learning Algorithms

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Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control

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Learning to use chopsticks in diverse gripping styles

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Neural3Points: Learning to Generate Physically Realistic Full‐body Motion for Virtual Reality Users

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AdaptNet: Policy Adaptation for Physics-Based Character Control

Xu, P., Xie, K., Andrews, S., Kry, P. G., Neff, M., McGuire, M., Karamouzas, I., & Zordan, V. B. (2023) · ACM Transactions on Graphics · doi:10.1145/3618375 · Cited by 21 · see citing works

Learning Human Dynamics in Autonomous Driving Scenarios

Wang, J., Yuan, Y., Luo, Z., Xie, K., Lin, D., Iqbal, U., Fidler, S., & Khamis, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01901 · Cited by 21 · see citing works

MuscleVAE: Model-Based Controllers of Muscle-Actuated Characters

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GPU-based contact-aware trajectory optimization using a smooth force model

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Autonomous Character-Scene Interaction Synthesis from Text Instruction

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Real-time optimization of active distribution networks with distributed energy resources participating in frequency regulation

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Intention-based Long-Term Human Motion Anticipation

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VMP: Versatile Motion Priors for Robustly Tracking Motion on Physical Characters

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Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels

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Strategy and Skill Learning for Physics-based Table Tennis Animation

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TokenHSI: Unified Synthesis of Physical Human-Scene Interactions through Task Tokenization

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Improving decision-making efficiency of image game based on deep Q-learning

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A causal convolutional neural network for multi-subject motion modeling and generation

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Plan Arithmetic: Compositional Plan Vectors for Multi-Task ControlPreprint

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Low Dimensional Motor Skill Learning Using Coactivation

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Human Motion Control of Quadrupedal Robots using Deep Reinforcement Learning

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DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics

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Learning to Walk With Deep Reinforcement Learning: Forward Dynamic Simulation of a Physics-Based Musculoskeletal Model of an Osseointegrated Transfemoral Amputee

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