48 citations · 54 across the 14 of their papers we have counts for
16 papers
DiVeR: Decision-Critical Verifier Learning for VLA Test-Time Scaling
Seongheon Park, Heecheol Kim, Shulin Tian +5
Scaling robot data and model capacity has improved Vision-Language-Action (VLA) policies, but further progress is constrained by the high cost of robotic data. Verifier-guided test…
VLaRL: Augmenting Vision-Language-Action Models with Simulation-Trained Latent-Conditioned Residual RL
Namiko Saito, Kinam Kim, Heecheol Kim +2
Vision-language-action (VLA) models provide broad, instruction-conditioned manipulation behaviors, but their physical execution can remain imprecise during contact-rich interaction…
ConCent: Contact-Centric Real-to-Sim-to-Real Learning from One Demonstration
Heecheol Kim, Namiko Saito, Katsushi Ikeuchi +1
Sim-to-real policy transfer -- deploying policies trained in simulation in the real world -- is a promising paradigm for scaling robot manipulation without large-scale real-world d…
Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement
Kinam Kim, Namiko Saito, Heecheol Kim +3
Vision-Language-Action (VLA) models can generalize across diverse manipulation tasks, but their imitation-learning-based policies remain brittle in precise physical interactions du…
IK Seed Generator for Dual-Arm Human-like Physicality Robot with Mobile Base
Jun Takamatsu, Atsushi Kanehira, Kazuhiro Sasabuchi +2
Robots are strongly expected as a means of replacing human tasks. If a robot has a human-like physicality, the possibility of replacing human tasks increases. In the case of househ…
RL-Driven Data Generation for Robust Vision-Based Dexterous Grasping
Atsushi Kanehira, Naoki Wake, Kazuhiro Sasabuchi +2
This work presents reinforcement learning (RL)-driven data augmentation to improve the generalization of vision-action (VA) models for dexterous grasping. While real-to-sim-to-real…
A Taxonomy of Self-Handover
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi +2
Self-handover, transferring an object between one's own hands, is a common but understudied bimanual action. While it facilitates seamless transitions in complex tasks, the strateg…
Plan-and-Act using Large Language Models for Interactive Agreement
Kazuhiro Sasabuchi, Naoki Wake, Atsushi Kanehira +2
Recent large language models (LLMs) are capable of planning robot actions. In this paper, we explore how LLMs can be used for planning actions with tasks involving situational huma…
Agreeing to Interact in Human-Robot Interaction using Large Language Models and Vision Language Models
Kazuhiro Sasabuchi, Naoki Wake, Atsushi Kanehira +2
In human-robot interaction (HRI), the beginning of an interaction is often complex. Whether the robot should communicate with the human is dependent on several situational factors…
VLM-driven Behavior Tree for Context-aware Task Planning
Naoki Wake, Atsushi Kanehira, Jun Takamatsu +2
The use of Large Language Models (LLMs) for generating Behavior Trees (BTs) has recently gained attention in the robotics community, yet remains in its early stages of development.…
Modality-Driven Design for Multi-Step Dexterous Manipulation: Insights from Neuroscience
Naoki Wake, Atsushi Kanehira, Daichi Saito +4
Multi-step dexterous manipulation is a fundamental skill in household scenarios, yet remains an underexplored area in robotics. This paper proposes a modular approach, where each s…
Open-Vocabulary Action Localization with Iterative Visual Prompting
Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi +2
Video action localization aims to find the timings of specific actions from a long video. Although existing learning-based approaches have been successful, they require annotating…
APriCoT: Action Primitives based on Contact-state Transition for In-Hand Tool Manipulation
Daichi Saito, Atsushi Kanehira, Kazuhiro Sasabuchi +4
In-hand tool manipulation is an operation that not only manipulates a tool within the hand (i.e., in-hand manipulation) but also achieves a grasp suitable for a task after the mani…
Designing Library of Skill-Agents for Hardware-Level Reusability
Jun Takamatsu, Daichi Saito, Katsushi Ikeuchi +3
To use new robot hardware in a new environment, it is necessary to develop a control program tailored to that specific robot in that environment. Considering the reusability of sof…
An Interactive Agent Foundation Model
Zane Durante, Bidipta Sarkar, Ran Gong +17
The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide…
Agent AI: Surveying the Horizons of Multimodal Interaction
Zane Durante, Qiuyuan Huang, Naoki Wake +11
Multi-modal AI systems will likely become a ubiquitous presence in our everyday lives. A promising approach to making these systems more interactive is to embody them as agents wit…