5 papers
ViSA: Visited-State Augmentation for Generalized Goal-Space Contrastive Reinforcement Learning
Issa Nakamura, Tomoya Yamanokuchi, Yuki Kadokawa +5
Goal-Conditioned Reinforcement Learning (GCRL) is a framework for learning a policy that can reach arbitrarily given goals. In particular, Contrastive Reinforcement Learning (CRL)…
VoxelDiffusionCut: Non-destructive Internal-part Extraction via Iterative Cutting and Structure Estimation
Takumi Hachimine, Yuhwan Kwon, Cheng-Yu Kuo +2
Non-destructive extraction of the target internal part, such as batteries and motors, by cutting surrounding structures is crucial at recycling and disposal sites. However, the div…
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…