Large Language Models (LLMs) have demonstrated impressive planning abilities in single-
agent embodied tasks across various domains. However, their capacity for planning and …

Large Language Models (LLMs) have the capacity of performing complex scheduling in a
multi-agent system and can coordinate these agents into completing sophisticated tasks that …

Developmental psychologists have long-established the importance of socio-cognitive
abilities in human intelligence. These abilities enable us to enter, participate and benefit …

People often give instructions whose meaning is ambiguous without further context,
expecting that their actions or goals will disambiguate their intentions. How can we build …

We present a systematic framework designed to enhance human-robot perception and
collaboration through the integration of logical rules and Theory of Mind (ToM). Logical rules …

Realizing scaling laws in embodied AI has become a focus. However, previous work has
been scattered across diverse simulation platforms, with assets and models lacking unified …

For AI agents to be helpful to humans, they should be able to follow natural language
instructions to complete everyday cooperative tasks in human environments. However, real …

Situation recognition refers to the ability of an agent to identify and understand various
situations or contexts based on available information and sensory inputs. It involves the …

Map representation learned by expert demonstrations has shown promising research value.
However, recent advancements in the visual navigation field face challenges due to the lack …

Language-guided robotic manipulation is a challenging task that requires an embodied
agent to follow abstract user instructions to accomplish various complex manipulation tasks …