3 citations · 4 across the 8 of their papers we have counts for
10 papers
Functional Cache Grafting: Robust and Rapid Code-Policy Synthesis for Embodied Agents
Saehun Chun, Wonje Choi, Sera Choi +2
Code-writing large language models (CodeLLMs) generate executable code policies for embodied agents by translating natural language goals and environmental constraints into structu…
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…
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…
Policy Compatible Skill Incremental Learning via Lazy Learning Interface
Daehee Lee, Dongsu Lee, TaeYoon Kwack +2
Skill Incremental Learning (SIL) is the process by which an embodied agent expands and refines its skill set over time by leveraging experience gained through interaction with its…
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,…
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
Wonje Choi, Woo Kyung Kim, SeungHyun Kim +1
For embodied reinforcement learning (RL) agents interacting with the environment, it is desirable to have rapid policy adaptation to unseen visual observations, but achieving zero-…
Incremental Learning of Retrievable Skills For Efficient Continual Task Adaptation
Daehee Lee, Minjong Yoo, Woo Kyung Kim +2
Continual Imitation Learning (CiL) involves extracting and accumulating task knowledge from demonstrations across multiple stages and tasks to achieve a multi-task policy. With rec…