8,107 research outputs found
An Efficiently Solvable Quadratic Program for Stabilizing Dynamic Locomotion
We describe a whole-body dynamic walking controller implemented as a convex
quadratic program. The controller solves an optimal control problem using an
approximate value function derived from a simple walking model while respecting
the dynamic, input, and contact constraints of the full robot dynamics. By
exploiting sparsity and temporal structure in the optimization with a custom
active-set algorithm, we surpass the performance of the best available
off-the-shelf solvers and achieve 1kHz control rates for a 34-DOF humanoid. We
describe applications to balancing and walking tasks using the simulated Atlas
robot in the DARPA Virtual Robotics Challenge.Comment: 6 pages, published at ICRA 201
Unsupervised 3D Pose Estimation with Geometric Self-Supervision
We present an unsupervised learning approach to recover 3D human pose from 2D
skeletal joints extracted from a single image. Our method does not require any
multi-view image data, 3D skeletons, correspondences between 2D-3D points, or
use previously learned 3D priors during training. A lifting network accepts 2D
landmarks as inputs and generates a corresponding 3D skeleton estimate. During
training, the recovered 3D skeleton is reprojected on random camera viewpoints
to generate new "synthetic" 2D poses. By lifting the synthetic 2D poses back to
3D and re-projecting them in the original camera view, we can define
self-consistency loss both in 3D and in 2D. The training can thus be self
supervised by exploiting the geometric self-consistency of the
lift-reproject-lift process. We show that self-consistency alone is not
sufficient to generate realistic skeletons, however adding a 2D pose
discriminator enables the lifter to output valid 3D poses. Additionally, to
learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter
network to allow for an expansion of 2D data. This improves results and
demonstrates the usefulness of 2D pose data for unsupervised 3D lifting.
Results on Human3.6M dataset for 3D human pose estimation demonstrate that our
approach improves upon the previous unsupervised methods by 30% and outperforms
many weakly supervised approaches that explicitly use 3D data
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