Hirotaka Tahara — accepted AI-conference papers

2026

Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning (I)

Yuki Kadokawa, Hirotaka Tahara, Takamitsu Matsubara

ICRA 2026poster

In earthwork and construction, excavators often encounter large rocks mixed with various soil conditions, requiring skilled operators. This paper presents a framework for achieving autonomous excavation using reinforcement learning (RL) through a rock excavation simulator. In the simulation, resolut…

Cited by 0 Scholar ↗

2023

Disturbance Injection Under Partial Automation: Robust Imitation Learning for Long-Horizon Tasks

Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh, Edgar Anarossi, Takamitsu Matsubara

RA-L 2023

Partial Automation (PA) with intelligent support systems has been introduced in industrial machinery and advanced automobiles to reduce the burden of long hours of human operation. Under PA, operators perform manual operations (providing actions) and operations that switch to automatic/manual mode (

Cited by 5 Source ↗Scholar ↗

2022

Disturbance-injected Robust Imitation Learning with Task Achievement

Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh, Brendan Michael, Takamitsu Matsubara

ICRA 2022poster

Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that policy stabilization can be learned via simple augmentations. In real-world scenarios, demonstrations are often of divers…

Cited by 13 Source ↗Scholar ↗