DeepAI AI Chat
Log In Sign Up

Multimodal Trajectory Optimization for Motion Planning

by   Takayuki Osa, et al.

Existing motion planning methods often have two drawbacks: 1) goal configurations need to be specified by a user, and 2) only a single solution is generated under a given condition. In practice, multiple possible goal configurations exist to achieve a task. Although the choice of the goal configuration significantly affects the quality of the resulting trajectory, it is not trivial for a user to specify the optimal goal configuration. In addition, the objective function used in the trajectory optimization is often non-convex, and it can have multiple solutions that achieve comparable costs. In this study, we propose a framework that determines multiple trajectories that correspond to the different modes of the cost function. We reduce the problem of identifying the modes of the cost function to that of estimating the density induced by a distribution based on the cost function. The proposed framework enables users to select a preferable solution from multiple candidate trajectories, thereby making it easier to tune the cost function and obtain a satisfactory solution. We evaluated our proposed method with motion planning tasks in 2D and 3D space. Our experiments show that the proposed algorithm is capable of determining multiple solutions for those tasks.


page 1

page 6

page 7

page 11

page 12

page 13

page 14


Cost-to-Go Function Generating Networks for High Dimensional Motion Planning

This paper presents c2g-HOF networks which learn to generate cost-to-go ...

Tensor Train for Global Optimization Problems in Robotics

The convergence of many numerical optimization techniques is highly sens...

Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions

The problem of identifying algorithmic recourse for people affected by m...

Motion Planning by Learning the Solution Manifold in Trajectory Optimization

The objective function used in trajectory optimization is often non-conv...

Discovering Multiple Algorithm Configurations

Many practitioners in robotics regularly depend on classic, hand-designe...

TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions

Trajectory optimization is an important tool for control and planning of...

Learning the Solution Manifold in Optimization and Its Application in Motion Planning

Optimization is an essential component for solving problems in wide-rang...