Towards Combining Motion Optimization and Data Driven Dynamical Models for Human Motion Prediction

Predicting human motion in unstructured and dynamic environments is challenging. Human behavior arises from complex sensory-motor couplings processes that can change drastically depending on environments or tasks. In order to alleviate this issue, we propose to encode the lower level aspects of human motion separately from the higher level geometrical aspects using data driven dynamical models. In order to perform longer term behavior predictions that account for variation in tasks and environments, we propose to make use of gradient based constraint motion optimization. The present method is the first to our knowledge to combine motion optimization and data driven dynamical models for human motion prediction. We present results on synthetic and motion capture data of upper body reaching movements (see Figure 1) that demonstrate the efficacy of the approach with respect to simple baselines often mentioned in prior work.

[1]  Jitendra Malik,et al.  Recurrent Network Models for Human Dynamics , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[2]  Daniel Sbarbaro,et al.  Nonlinear adaptive control using non-parametric Gaussian Process prior models , 2002 .

[3]  Dmitry Berenson,et al.  Human-robot collaborative manipulation planning using early prediction of human motion , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Stefan Schaal,et al.  Warping the workspace geometry with electric potentials for motion optimization of manipulation tasks , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[5]  Sebastian Nowozin,et al.  Efficient Nonlinear Markov Models for Human Motion , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Hema Swetha Koppula,et al.  Anticipating Human Activities Using Object Affordances for Reactive Robotic Response , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Andrew Gordon Wilson,et al.  Learning Scalable Deep Kernels with Recurrent Structure , 2016, J. Mach. Learn. Res..

[8]  Dieter Kraft,et al.  Algorithm 733: TOMP–Fortran modules for optimal control calculations , 1994, TOMS.

[9]  Angela P. Schoellig,et al.  Safe and robust learning control with Gaussian processes , 2015, 2015 European Control Conference (ECC).

[10]  Michael J. Black,et al.  On Human Motion Prediction Using Recurrent Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Rajesh P. N. Rao,et al.  Robotic imitation from human motion capture using Gaussian processes , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[12]  Sergey Levine,et al.  Learning Robust Rewards with Adversarial Inverse Reinforcement Learning , 2017, ICLR 2017.

[13]  Dana Kulic,et al.  Incremental learning of full body motion primitives and their sequencing through human motion observation , 2012, Int. J. Robotics Res..

[14]  Siddhartha S. Srinivasa,et al.  CHOMP: Covariant Hamiltonian optimization for motion planning , 2013, Int. J. Robotics Res..

[15]  Dmitry Berenson,et al.  Goal Set Inverse Optimal Control and Iterative Replanning for Predicting Human Reaching Motions in Shared Workspaces , 2016, IEEE Transactions on Robotics.

[16]  David J. Fleet,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .

[17]  Francesco Nori,et al.  Evidence for Composite Cost Functions in Arm Movement Planning: An Inverse Optimal Control Approach , 2011, PLoS Comput. Biol..

[18]  Marc Toussaint.  A Tutorial on Newton Methods for Constrained Trajectory Optimization and Relations to SLAM, Gaussian Process Smoothing, Optimal Control, and Probabilistic Inference , 2017, Geometric and Numerical Foundations of Movements.