71 citations · 100 across the 5 of their papers we have counts for
8 papers
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…
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Johannes Ackermann, Volker Gabler, Takayuki Osa +1
Many real world tasks require multiple agents to work together. Multi-agent reinforcement learning (RL) methods have been proposed in recent years to solve these tasks, but current…
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…
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of…