28 citations · 132 across the 19 of their papers we have counts for
8 papers · 1 filter
Dynamic Coloring and Graph Squares of Regular Graphs
Juan Gutierrez, Grover Ugarte
An -dynamic coloring of a graph is a proper vertex coloring in which every vertex sees at least distinct colors in its neighborhood. The minimum number…
Transporting Unsecured Stacked Payloads with a Quadrupedal Robot via Multi-Objective Reinforcement Learning
Nobuo Namura, Masayuki Hiromoto, Kento Uemura +2
Transporting unsecured payloads with legged robots over uneven terrain requires balancing locomotion performance and payload stability, since aggressive motion can destabilize the…
Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models
Shaunak A. Mehta, Ananya Hazarika, Haochen Zhang +5
For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those actions. Rapid progress in the…
Co-training with Ego-centric Video and Demonstration for Robot Navigation Task
Shoya Kuno, Yumo Ouchi, Kanata Suzuki
Vision-language-action (VLA) models are promising for diverse robotic tasks, but their performance heavily depends on large-scale high-quality training data, whose collection on re…
How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism
Kana Miyamoto, Kanata Suzuki, Tetsuya Ogata
Imitation learning for robotic tasks has relied primarily on policies trained only on successful demonstrations, although failures are unavoidable during human data collection. Man…
From Dialogue to Execution: Mixture-of-Agents Assisted Interactive Planning for Behavior Tree-Based Long-Horizon Robot Execution
Kanata Suzuki, Kazuki Hori, Haruka Miyoshi +2
Interactive task planning with large language models (LLMs) lets robots generate high-level action plans from natural language, but over long horizons it asks many questions, and t…
Compact Task-Aligned Imitation Learning for Laboratory Automation
Kanata Suzuki, Hanon Nakamura, Kana Miyamoto +1
Robotic laboratory automation has traditionally relied on carefully engineered motion pipelines and task-specific hardware interfaces, resulting in high design cost and limited fle…
Proprioception Enhances Vision Language Model in Generating Captions and Subtask Segmentations for Robot Task
Kanata Suzuki, Shota Shimizu, Tetsuya Ogata
From the perspective of future developments in robotics, it is crucial to verify whether foundation models trained exclusively on offline data, such as images and language, can und…