6 papers
KeyMPs: One-Shot Vision-Language Guided Motion Generation by Sequencing DMPs for Occlusion-Rich Tasks
Edgar Anarossi, Yuhwan Kwon, Hirotaka Tahara +6
Dynamic Movement Primitives (DMPs) provide a flexible framework wherein smooth robotic motions are encoded into modular parameters. However, they face challenges in integrating mul…
BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation
Hirotaka Tahara, Takamitsu Matsubara
Applying imitation learning (IL) is challenging to nonprehensile manipulation tasks of invisible objects with partial observations, such as excavating buried rocks. The demonstrato…
Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning
Yuki Kadokawa, Hirotaka Tahara, Takamitsu Matsubara
In earthwork and construction, excavators often encounter large rocks mixed with various soil conditions, requiring skilled operators. This paper presents a framework for achieving…
Deep Segmented DMP Networks for Learning Discontinuous Motions
Edgar Anarossi, Hirotaka Tahara, Naoto Komeno +1
Discontinuous motion which is a motion composed of multiple continuous motions with sudden change in direction or velocity in between, can be seen in state-aware robotic tasks. Suc…
Disturbance Injection under Partial Automation: Robust Imitation Learning for Long-horizon Tasks
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh +2
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.…
Disturbance-Injected Robust Imitation Learning with Task Achievement
Hirotaka Tahara, Hikaru Sasaki, Hanbit Oh +2
Robust imitation learning using disturbance injections overcomes issues of limited variation in demonstrations. However, these methods assume demonstrations are optimal, and that p…