4 research outputs found
An Interior Point Method Solving Motion Planning Problems with Narrow Passages
Algorithmic solutions for the motion planning problem have been investigated
for five decades. Since the development of A* in 1969 many approaches have been
investigated, traditionally classified as either grid decomposition, potential
fields or sampling-based. In this work, we focus on using numerical
optimization, which is understudied for solving motion planning problems. This
lack of interest in the favor of sampling-based methods is largely due to the
non-convexity introduced by narrow passages. We address this shortcoming by
grounding the solution in differential geometry. We demonstrate through a
series of experiments on 3 Dofs and 6 Dofs narrow passage problems, how
modeling explicitly the underlying Riemannian manifold leads to an efficient
interior-point non-linear programming solution.Comment: IEEE RO-MAN 2020, 6 page
Hierarchical Human-Motion Prediction and Logic-Geometric Programming for Minimal Interference Human-Robot Tasks
In this paper, we tackle the problem of human-robot coordination in sequences
of manipulation tasks. Our approach integrates hierarchical human motion
prediction with Task and Motion Planning (TAMP). We first devise a hierarchical
motion prediction approach by combining Inverse Reinforcement Learning and
short-term motion prediction using a Recurrent Neural Network. In a second
step, we propose a dynamic version of the TAMP algorithm Logic-Geometric
Programming (LGP). Our version of Dynamic LGP, replans periodically to handle
the mismatch between the human motion prediction and the actual human behavior.
We assess the efficacy of the approach by training the prediction algorithms
and testing the framework on the publicly available MoGaze dataset.Comment: 8 pages, accepted to IEEE-ROMAN 202