5 accepted papers
Vision-Language Models (VLMs) have demonstrated exceptional general reasoning capabilities. However, their performance in embodied navigation remains hindered by a scarcity of aligned open-world vision and robot control data. Despite simulators providing a cost-effective alternative for data collect…
In embodied vision, Goal-Oriented Navigation (GON) requires robots to locate a specific goal within an unexplored environment. The primary challenge of GON arises from the need to construct a Bird's-Eye-View (BEV) map to understand the environment while simultaneously localizing an unobserved goal. …
Embodied Question Answering (EQA) is a task in artificial intelligence where an intelligent agent is required to answer questions about its environment. For example, to answer a question such as "Is the TV on or off?", the agent must navigate to the room with the TV and answer with either "On." or "…
Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited explo…
Reward acts as a signal to guide the agent’s learning process in Reinforcement Learning (RL), evaluating and assigning rewards to the agent’s actions based on theiralignment with goals. Designing reward is challenging in multiagent environment such as StarCraft II benchmark since agents face credit…