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

Honors & Awards

  • SIGGRAPH Significant New Research Award, ACM (2012)
  • Alfred P. Sloan Research Fellowship, Alfred P. Sloan Foundation (2010)
  • Young Innovators Under 35, MIT Technology Review (2007)
  • CAREER Award, National Science Foundation (2007)

Professional Education

  • BS, National Taiwan University, Computer Science (1999)
  • MS, University of Washington, Computer Science (2001)
  • PhD, University of Washington, Computer Science (2005)

Contact

  • Academic
    ckliu38@stanford.edu
    University - Faculty Department: Computer Science Position: Assoc Professor

Additional Info

  • Mail Code: 9035

Links

2020-21 Courses

Stanford Advisees

All Publications

  • Learning to Manipulate Amorphous Materials ACM TRANSACTIONS ON GRAPHICS Zhang, Y., Yu, W., Liu, C., Kemp, C., Turk, G. 2020; 39 (6)

    View details for DOI 10.1145/3414685.3417868

    View details for Web of Science ID 000595589100029

  • Learning to Collaborate From Simulation for Robot-Assisted Dressing IEEE ROBOTICS AND AUTOMATION LETTERS Clegg, A., Erickson, Z., Grady, P., Turk, G., Kemp, C. C., Liu, C. 2020; 5 (2): 2746–53

    View details for DOI 10.1109/LRA.2020.2972852

    View details for Web of Science ID 000526702500017

  • Learning a Control Policy for Fall Prevention on an Assistive Walking Device Kumar, V. C., Ha, S., Sawicki, G., Liu, C. K. 2020

    View details for DOI 10.1109/ICRA40945.2020.9196798

  • Assistive Gym: A Physics Simulation Framework for Assistive Robotics Erickson, Z., Gangaram, V., Kapusta, A., Liu, C. K., Kemp, C. C. 2020

    View details for DOI 10.1109/ICRA40945.2020.9197411

  • Visualizing Movement Control Optimization Landscapes. IEEE transactions on visualization and computer graphics Hamalainen, P. n., Toikka, J. n., Babadi, A. n., Liu, K. n. 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

  • Estimating Mass Distribution of Articulated Objects using Non-prehensile Manipulation Kumar, K. N., Essa, I., Ha, S., Liu, C. K. 2020
  • Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data Clever, H. M., Erickson, Z., Kapusta, A., Turk, G., Liu, C., Kemp, C. C., IEEE IEEE. 2020: 6214–23

    View details for DOI 10.1109/CVPR42600.2020.00625

    View details for Web of Science ID 000620679506049

  • Learning a Control Policy for Fall Prevention on an Assistive Walking Device Kumar, V., Ha, S., Sawicki, G., Liu, C. K. 2020

    View details for DOI 10.1109/ICRA40945.2020.9196798

  • Personalized collaborative plans for robot-assisted dressing via optimization and simulation AUTONOMOUS ROBOTS Kapusta, A., Erickson, Z., Clever, H. M., Yu, W., Liu, C., Turk, G., Kemp, C. C. 2019; 43 (8): 2183–2207

    View details for DOI 10.1007/s10514-019-09865-0

    View details for Web of Science ID 000487951900014

  • Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation ACM TRANSACTIONS ON GRAPHICS Jiang, Y., Van Wouwe, T., De Groote, F., Liu, C. 2019; 38 (4)

    View details for DOI 10.1145/3306346.3322966

    View details for Web of Science ID 000475740600046

  • Sim-to-Real Transfer for Biped Locomotion IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Yu, W., Kumar, V. C., Turk, G., Liu, C. 2019

    View details for DOI 10.1109/IROS40897.2019.8968053

  • Policy Transfer with Strategy Optimization Yu, W., Liu, C., Turk, G. 2019
  • Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing Erickson, Z., Clever, H. M., Gangaram, V., Turk, G., Liu, C., Kemp, C. C. 2019