5 papers
ReAcTree: Hierarchical LLM Agent Trees with Control Flow for Long-Horizon Task Planning
Jae-Woo Choi, Hyungmin Kim, Hyobin Ong +4
Recent advancements in large language models (LLMs) have enabled significant progress in decision-making and task planning for embodied autonomous agents. However, most existing me…
Learning Dexterous Bimanual Catch Skills through Adversarial-Cooperative Heterogeneous-Agent Reinforcement Learning
Taewoo Kim, Youngwoo Yoon, Jaehong Kim
Robotic catching has traditionally focused on single-handed systems, which are limited in their ability to handle larger or more complex objects. In contrast, bimanual catching off…
Model Comparison for Fast Domain Adaptation in Table Service Scenario
Woo-han Yun, Minsu Jang, Jaehong Kim
In restaurants, many aspects of customer service, such as greeting customers, taking orders, and processing payments, are automated. Due to the various cuisines, required services,…
LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents
Jae-Woo Choi, Youngwoo Yoon, Hyobin Ong +2
Large language models (LLMs) have recently received considerable attention as alternative solutions for task planning. However, comparing the performance of language-oriented task…
Design and Evaluation of an Uncertainty-Aware Shared-Autonomy System with Hierarchical Conservative Skill Inference
Taewoo Kim, Donghyung Kim, Minsu Jang +1
Shared-autonomy imitation learning lets a human correct a robot in real time, mitigating covariate-shift errors. Yet existing approaches ignore two critical factors: (i) the operat…