Embodied Question Answering
8 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Embodied Question Answering
EQA
OpenEQA
Latest papers with no code
Explore until Confident: Efficient Exploration for Embodied Question Answering
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question.
LLM as A Robotic Brain: Unifying Egocentric Memory and Control
In this paper, we propose a novel and generalizable framework called LLM-Brain: using Large-scale Language Model as a robotic brain to unify egocentric memory and control.
Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation
PEMR includes a "looking ahead" process, \textit{i. e.} a visual feature extractor module that estimates feasible paths for gathering 3D navigational information, which is mimicking the human sense of direction.
A Survey of Embodied AI: From Simulators to Research Tasks
This paper aims to provide an encyclopedic survey for the field of embodied AI, from its simulators to its research.
Counterfactual Vision-and-Language Navigation: Unravelling the Unseen
The task of vision-and-language navigation (VLN) requires an agent to follow text instructions to find its way through simulated household environments.
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering
Then by the guide of extracted semantic features, a bottom-up visual attention mechanism is proposed for the Visual Question Answering (VQA) sub-task.
Cross-Task Knowledge Transfer for Visually-Grounded Navigation
Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for two different tasks: learning to follow navigational instructions and embodied question answering.
Revisiting EmbodiedQA: A Simple Baseline and Beyond
In this paper, we empirically study this problem and introduce 1) a simple yet effective baseline that achieves promising performance; 2) an easier and practical setting for EmbodiedQA where an agent has a chance to adapt the trained model to a new environment before it actually answers users questions.
Embodied Question Answering in Photorealistic Environments with Point Cloud Perception
To help bridge the gap between internet vision-style problems and the goal of vision for embodied perception we instantiate a large-scale navigation task -- Embodied Question Answering [1] in photo-realistic environments (Matterport 3D).
Embodied Multimodal Multitask Learning
In this paper, we propose a multitask model capable of jointly learning these multimodal tasks, and transferring knowledge of words and their grounding in visual objects across the tasks.
