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Visual Compliance via Executable Safety Rule Entailment
Jisoo Kim, TaeYoon Kwack, Jinwoo Jang +1
Recent advances in LLMs and VLMs have enabled safety systems to reason beyond simple risk patterns toward more contextual and semantic safety concerns. However, as risk patterns co…
Multi-scale Mixture of World Models for Embodied Agents in Evolving Environments
Jinwoo Jang, Daniel J. Rho, Sihyung Yoon +2
Embodied agents operating in the real world require multi-scale reasoning and knowledge adaptation as conditions change. We identify two challenges in applying Mixture of Experts (…
Efficient Skill Grounding via Code Refactoring with Small Language Models
Sera Choi, Wonje Choi, Saehun Chun +4
Effective skill grounding is essential for deploying reusable skills in embodied agents, as even minor embodiment or environmental differences can render an entire skill incompatib…
Cross-Domain Demo-to-Code via Neurosymbolic Counterfactual Reasoning
Jooyoung Kim, Wonje Choi, Younguk Song +1
Recent advances in Vision-Language Models (VLMs) have enabled video-instructed robotic programming, allowing agents to interpret video demonstrations and generate executable contro…
Test-Time Mixture of World Models for Embodied Agents in Dynamic Environments
Jinwoo Jang, Minjong Yoo, Sihyung Yoon +1
Language model (LM)-based embodied agents are increasingly deployed in real-world settings. Yet, their adaptability remains limited in dynamic environments, where constructing accu…
Towards Reliable Code-as-Policies: A Neuro-Symbolic Framework for Embodied Task Planning
Sanghyun Ahn, Wonje Choi, Junyong Lee +2
Recent advances in large language models (LLMs) have enabled the automatic generation of executable code for task planning and control in embodied agents such as robots, demonstrat…
NeSyPr: Neurosymbolic Proceduralization For Efficient Embodied Reasoning
Wonje Choi, Jooyoung Kim, Honguk Woo
We address the challenge of adopting language models (LMs) for embodied tasks in dynamic environments, where online access to large-scale inference engines or symbolic planners is…
Exploratory Retrieval-Augmented Planning For Continual Embodied Instruction Following
Minjong Yoo, Jinwoo Jang, Wei-jin Park +1
This study presents an Exploratory Retrieval-Augmented Planning (ExRAP) framework, designed to tackle continual instruction following tasks of embodied agents in dynamic, non-stati…
World Model Implanting for Test-time Adaptation of Embodied Agents
Minjong Yoo, Jinwoo Jang, Sihyung Yoon +1
In embodied AI, a persistent challenge is enabling agents to robustly adapt to novel domains without requiring extensive data collection or retraining. To address this, we present…
Unifying Agent Interaction and World Information for Multi-agent Coordination
Dongsu Lee, Daehee Lee, Yaru Niu +3
This work presents a novel representation learning framework, *interaction-world* latent (IWoL), to facilitate *team coordination* in multi-agent reinforcement learning (MARL). Bui…
NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks In Open Domains
Wonje Choi, Jinwoo Park, Sanghyun Ahn +2
We explore neuro-symbolic approaches to generalize actionable knowledge, enabling embodied agents to tackle complex tasks more effectively in open-domain environments. A key challe…
Embodied CoT Distillation From LLM To Off-the-shelf Agents
Wonje Choi, Woo Kyung Kim, Minjong Yoo +1
We address the challenge of utilizing large language models (LLMs) for complex embodied tasks, in the environment where decision-making systems operate timely on capacity-limited,…
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble
Yujeong Lee, Sangwoo Shin, Wei-Jin Park +1
Employing large language models (LLMs) to enable embodied agents has become popular, yet it presents several limitations in practice. In this work, rather than using LLMs directly…
Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments
Sangwoo Shin, Seunghyun Kim, Youngsoo Jang +2
In embodied instruction-following (EIF), the integration of pretrained language models (LMs) as task planners emerges as a significant branch, where tasks are planned at the skill…
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation
Sangwoo Shin, Minjong Yoo, Jeongwoo Lee +1
This work explores the zero-shot adaptation capability of semantic skills, semantically interpretable experts' behavior patterns, in cross-domain settings, where a user input in in…