5 papers
Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few Examples
Taewoong Kim, Byeonghwi Kim, Jonghyun Choi
Learning a perception and reasoning module for robotic assistants to plan steps to perform complex tasks based on natural language instructions often requires large free-form langu…
ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic Environments
Taewoong Kim, Cheolhong Min, Byeonghwi Kim +3
Simulated virtual environments have been widely used to learn robotic agents that perform daily household tasks. These environments encourage research progress by far, but often pr…
Online Continual Learning For Interactive Instruction Following Agents
Byeonghwi Kim, Minhyuk Seo, Jonghyun Choi
In learning an embodied agent executing daily tasks via language directives, the literature largely assumes that the agent learns all training data at the beginning. We argue that…
Multi-Level Compositional Reasoning for Interactive Instruction Following
Suvaansh Bhambri, Byeonghwi Kim, Jonghyun Choi
Robotic agents performing domestic chores by natural language directives are required to master the complex job of navigating environment and interacting with objects in the enviro…
Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents
Byeonghwi Kim, Jinyeon Kim, Yuyeong Kim +2
Accomplishing household tasks requires to plan step-by-step actions considering the consequences of previous actions. However, the state-of-the-art embodied agents often make mista…