5 papers
DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of Mass
Tomoya Yamanokuchi, Alberto Bacchin, Emilio Olivastri +3
In this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface whi…
Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning
Tomoya Yamanokuchi, Alberto Bacchin, Emilio Olivastri +2
In this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface whi…
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…
Self-Supervised Learning of Grasping Arbitrary Objects On-the-Move
Takuya Kiyokawa, Eiki Nagata, Yoshihisa Tsurumine +2
Mobile grasping enhances manipulation efficiency by utilizing robots' mobility. This study aims to enable a commercial off-the-shelf robot for mobile grasping, requiring precise ti…
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…