5 papers
RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories
Yunfu Deng, Sangwoo Shin, Brahma S. Pavse +1
Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world models. These rollout-based app…
Articulated-Body Dynamics Network: Dynamics-Grounded Prior for Robot Learning
Sangwoo Shin, Kunzhao Ren, Xiaobin Xiong +1
Recent work in reinforcement learning has shown that incorporating structural priors for articulated robots, such as link connectivity, into policy networks improves learning effic…
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…