382 citations · 550 across the 10 of their papers we have counts for
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Motion Planning by Learning the Solution Manifold in Trajectory Optimization
Takayuki Osa
The objective function used in trajectory optimization is often non-convex and can have an infinite set of local optima. In such cases, there are diverse solutions to perform a giv…
Learning the Solution Manifold in Optimization and Its Application in Motion Planning
Takayuki Osa
Optimization is an essential component for solving problems in wide-ranging fields. Ideally, the objective function should be designed such that the solution is unique and the opti…
Multimodal Trajectory Optimization for Motion Planning
Takayuki Osa
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…
Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes
Takayuki Osa, Shuhei Ikemoto
Imitation learning is an intuitive approach for teaching motion to robotic systems. Although previous studies have proposed various methods to model demonstrated movement primitive…
An Algorithmic Perspective on Imitation Learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann +3
As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingl…