4 papers
Feasibility-aware Imitation Learning from Observation with Multimodal Feedback
Kei Takahashi, Hikaru Sasaki, Takamitsu Matsubara
Imitation learning frameworks that learn robot control policies from demonstrators' motions via hand-mounted demonstration interfaces have attracted increasing attention. However,…
Robotic System for Chemical Experiment Automation with Dual Demonstration of End-effector and Jig Operations
Hikaru Sasaki, Naoto Komeno, Takumi Hachimine +9
While robotic automation has demonstrated remarkable performance, such as executing hundreds of experiments continuously over several days, designing synchronized motions between t…
Feasibility-aware Imitation Learning from Observations through a Hand-mounted Demonstration Interface
Kei Takahashi, Hikaru Sasaki, Takamitsu Matsubara
Imitation learning through a demonstration interface is expected to learn policies for robot automation from intuitive human demonstrations. However, due to the differences in huma…
Weber-Fechner Law in Temporal Difference learning derived from Control as Inference
Keiichiro Takahashi, Taisuke Kobayashi, Tomoya Yamanokuchi +1
This paper investigates a novel nonlinear update rule based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in the standard RL states that the TD…