6 citations · 6 across the 9 of their papers we have counts for
9 papers
Autonomous Obstacle Removal for Excavators through Policy Learning with Particle Simulation
Yuki Kadokawa, Sandro M. Alcantara Tacora, Taro Abe +4
Autonomous obstacle removal from the ground is an important earthwork task, but this is difficult to automate because an excavator must adapt its excavation trajectories over repea…
Bridged SBI: Correcting Biased Low-Fidelity Posteriors for Cost-Efficient High-Fidelity Inference
Gahee Kim, Yuki Kadokawa, Sandro M. Alcantara Tacora +5
Accurate calibration of particle-based simulators is crucial for robotic earthwork simulation, but analytical calibration is challenging due to this task's highly nonlinear particl…
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)…
DeReCo: Decoupling Representation and Coordination Learning for Object-Adaptive Decentralized Multi-Robot Cooperative Grasping and Lifting
Kazuki Shibata, Ryosuke Sota, Shandil Dhiresh Bosch +3
Generalizing decentralized multi-robot cooperative grasping and lifting across objects with diverse shapes and physical properties remains a fundamental challenge. Under decentrali…
CoLF: Learning Consistent Leader-Follower Policies for Vision-Language-Guided Multi-Robot Cooperative Transport
Joachim Yann Despature, Kazuki Shibata, Takamitsu Matsubara
In this study, we address vision-language-guided multi-robot cooperative transport, where each robot grounds natural-language instructions from onboard camera observations. A key c…
OIPP: Object-Adaptive Impact Point Predictor for Catching Diverse In-Flight Objects
Ngoc Huy Nguyen, Kazuki Shibata, Takamitsu Matsubara
In this study, we address the problem of in-flight object catching using a quadruped robot with a basket. Our objective is to accurately predict the impact point, defined as the ob…
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control
Yoshiki Yano, Kazuki Shibata, Maarten Kokshoorn +1
Recent advances in Large Language Models (LLMs) have permitted the development of language-guided multi-robot systems, which allow robots to execute tasks based on natural language…
Task-priority Intermediated Hierarchical Distributed Policies: Reinforcement Learning of Adaptive Multi-robot Cooperative Transport
Yusei Naito, Tomohiko Jimbo, Tadashi Odashima +1
Multi-robot cooperative transport is crucial in logistics, housekeeping, and disaster response. However, it poses significant challenges in environments where objects of various we…
Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks
Yuma Shida, Tomohiko Jimbo, Tadashi Odashima +1
Multi-object transport using multi-robot systems has the potential for diverse practical applications such as delivery services owing to its efficient individual and scalable coope…