70 citations · 104 across the 6 of their papers we have counts for
6 papers
An Inference-Based Architecture for Intent and Affordance Saturation in Decision-Making
Wendyam Eric Lionel Ilboudo, Saori C Tanaka
Decision paralysis, i.e. hesitation, freezing, or failure to act despite full knowledge and motivation, poses a challenge for choice models that assume options are already specifie…
Domains as Objectives: Domain-Uncertainty-Aware Policy Optimization through Explicit Multi-Domain Convex Coverage Set Learning
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara
The problem of uncertainty is a feature of real world robotics problems and any control framework must contend with it in order to succeed in real applications tasks. Reinforcement…
AdaTerm: Adaptive T-Distribution Estimated Robust Moments for Noise-Robust Stochastic Gradient Optimization
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Takamitsu Matsubara
With the increasing practicality of deep learning applications, practitioners are inevitably faced with datasets corrupted by noise from various sources such as measurement errors,…
Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Kenji Sugimoto
Behavioral cloning (BC) bears a high potential for safe and direct transfer of human skills to robots. However, demonstrations performed by human operators often contain noise or i…
t-Soft Update of Target Network for Deep Reinforcement Learning
Taisuke Kobayashi, Wendyam Eric Lionel Ilboudo
This paper proposes a new robust update rule of target network for deep reinforcement learning (DRL), to replace the conventional update rule, given as an exponential moving averag…
TAdam: A Robust Stochastic Gradient Optimizer
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Kenji Sugimoto
Machine learning algorithms aim to find patterns from observations, which may include some noise, especially in robotics domain. To perform well even with such noise, we expect the…