
C. Karen Liu
Associate Professor of Computer Science
Bio
C. Karen Liu is an associate professor in the Computer Science Department at Stanford University. Prior to joining Stanford, Liu was a faculty member at the School of Interactive Computing at Georgia Tech. She received her Ph.D. degree in Computer Science from the University of Washington. Liu's research interests are in computer graphics and robotics, including physics-based animation, character animation, optimal control, reinforcement learning, and computational biomechanics. She developed computational approaches to modeling realistic and natural human movements, learning complex control policies for humanoids and assistive robots, and advancing fundamental numerical simulation and optimal control algorithms. The algorithms and software developed in her lab have fostered interdisciplinary collaboration with researchers in robotics, computer graphics, mechanical engineering, biomechanics, neuroscience, and biology. Liu received a National Science Foundation CAREER Award, an Alfred P. Sloan Fellowship, and was named Young Innovators Under 35 by Technology Review. In 2012, Liu received the ACM SIGGRAPH Significant New Researcher Award for her contribution in the field of computer graphics.
Academic Appointments
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Associate Professor, Computer Science
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Honors & Awards
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SIGGRAPH Significant New Research Award, ACM (2012)
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Alfred P. Sloan Research Fellowship, Alfred P. Sloan Foundation (2010)
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Young Innovators Under 35, MIT Technology Review (2007)
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CAREER Award, National Science Foundation (2007)
Professional Education
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BS, National Taiwan University, Computer Science (1999)
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MS, University of Washington, Computer Science (2001)
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PhD, University of Washington, Computer Science (2005)
2021-22 Courses
- Character Animation: Modeling, Simulation, and Control of Human Motion
CS 348E (Spr) - Computer Graphics in the Era of AI
CS 348I (Aut) -
Independent Studies (9)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Aut, Win) - Curricular Practical Training
CS 390A (Sum) - Curricular Practical Training
CS 390C (Sum) - Independent Project
CS 399 (Sum) - Independent Project
CS 399P (Spr) - Independent Work
CS 199 (Aut, Win, Spr) - Independent Work
CS 199P (Spr) - Part-time Curricular Practical Training
CS 390D (Win)
- Advanced Reading and Research
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Prior Year Courses
2020-21 Courses
- Character Animation: Modeling, Simulation, and Control of Human Motion
CS 348E (Spr) - Computer Graphics in the Era of AI
CS 348I (Aut)
2019-20 Courses
- Character Animation: Modeling, Simulation, and Control of Human Motion
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Nick Bianco, Michael Raitor, Davis Rempe -
Postdoctoral Faculty Sponsor
Seunghwan Lee, Jackson Wang, Zhaoming Xie -
Master's Program Advisor
David Lüdeke, Takara Truong -
Doctoral (Program)
Joao Araujo, Michelle Guo, Yifeng Jiang, Jiaman Li, Keenon Werling, Albert Wu
All Publications
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Learning Human Search Behavior from Egocentric Visual Inputs
COMPUTER GRAPHICS FORUM
2021; 40 (2): 389-398
View details for DOI 10.1111/cgf.142641
View details for Web of Science ID 000657959600032
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The Role of Physics-Based Simulators in Robotics
ANNUAL REVIEW OF CONTROL, ROBOTICS, AND AUTONOMOUS SYSTEMS, VOL 4, 2021
2021; 4: 35-58
View details for DOI 10.1146/annurev-control-072220-093055
View details for Web of Science ID 000652492900002
- Protective Policy Transfer 2021
- SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning 2021
- Policy Transfer via Kinematic Domain Randomization and Adaptation 2021
- Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact 2021
- Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments 2021
- Learning Task-Agnostic Action Spaces for Movement Optimization IEEE Transactions on Computer Graphics and Visualization 2021
- COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing 2021
- iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks 2021
- Co-GAIL Learning Diverse Strategies for Human-Robot Collaboration 2021
- BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments 2021
- DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI 2021
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Learning to Manipulate Amorphous Materials
ACM TRANSACTIONS ON GRAPHICS
2020; 39 (6)
View details for DOI 10.1145/3414685.3417868
View details for Web of Science ID 000595589100029
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Learning to Collaborate From Simulation for Robot-Assisted Dressing
IEEE ROBOTICS AND AUTOMATION LETTERS
2020; 5 (2): 2746–53
View details for DOI 10.1109/LRA.2020.2972852
View details for Web of Science ID 000526702500017
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Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
IEEE. 2020: 6214–23
View details for DOI 10.1109/CVPR42600.2020.00625
View details for Web of Science ID 000620679506049
- Estimating Mass Distribution of Articulated Objects using Non-prehensile Manipulation 2020
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Visualizing Movement Control Optimization Landscapes.
IEEE transactions on visualization and computer graphics
2020; PP
Abstract
A large body of animation research focuses on optimization of movement control, either as action sequences or policy parameters. However, as closed-form expressions of the objective functions are often not available, our understanding of the optimization problems is limited. Building on recent work on analyzing neural network training, we contribute novel visualizations of high-dimensional control optimization landscapes; this yields insights into why control optimization is hard and why common practices like early termination and spline-based action parameterizations make optimization easier. For example, our experiments show how trajectory optimization can become increasingly ill-conditioned with longer trajectories, but parameterizing control as partial target states-e.g., target angles converted to torques using a PD-controller-can act as an efficient preconditioner. Both our visualizations and quantitative empirical data also indicate that neural network policy optimization scales better than trajectory optimization for long planning horizons. Our work advances the understanding of movement optimization and our visualizations should also provide value in educational use.
View details for DOI 10.1109/TVCG.2020.3018187
View details for PubMedID 32816675
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Learning a Control Policy for Fall Prevention on an Assistive Walking Device
2020
View details for DOI 10.1109/ICRA40945.2020.9196798
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Learning a Control Policy for Fall Prevention on an Assistive Walking Device
2020
View details for DOI 10.1109/ICRA40945.2020.9196798
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Assistive Gym: A Physics Simulation Framework for Assistive Robotics
2020
View details for DOI 10.1109/ICRA40945.2020.9197411
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Personalized collaborative plans for robot-assisted dressing via optimization and simulation
AUTONOMOUS ROBOTS
2019; 43 (8): 2183–2207
View details for DOI 10.1007/s10514-019-09865-0
View details for Web of Science ID 000487951900014
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Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation
ACM TRANSACTIONS ON GRAPHICS
2019; 38 (4)
View details for DOI 10.1145/3306346.3322966
View details for Web of Science ID 000475740600046
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Sim-to-Real Transfer for Biped Locomotion
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2019
View details for DOI 10.1109/IROS40897.2019.8968053
- Policy Transfer with Strategy Optimization 2019
- Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing 2019