Embodied Question Answering
11 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Most implemented papers
Embodied Question Answering
We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where an agent is spawned at a random location in a 3D environment and asked a question ("What color is the car?").
Neural Modular Control for Embodied Question Answering
We use imitation learning to warm-start policies at each level of the hierarchy, dramatically increasing sample efficiency, followed by reinforcement learning.
Towards Learning a Generalist Model for Embodied Navigation
We conduct extensive experiments to evaluate the performance and generalizability of our model.
Blindfold Baselines for Embodied QA
We explore blindfold (question-only) baselines for Embodied Question Answering.
Multi-Target Embodied Question Answering
To address this, we propose a modular architecture composed of a program generator, a controller, a navigator, and a VQA module.
VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
The goal of this dataset is to assess question-answering performance from nearly-ideal navigation paths, while considering a much more complete variety of questions than current instantiations of the EQA task.
AllenAct: A Framework for Embodied AI Research
The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities.
Synthesizing Event-centric Knowledge Graphs of Daily Activities Using Virtual Space
Artificial intelligence (AI) is expected to be embodied in software agents, robots, and cyber-physical systems that can understand the various contextual information of daily life in the home environment to support human behavior and decision making in various situations.
MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments
To overcome this limitation, we introduce the Multimodal Embodied Interactive Agent (MEIA), capable of translating high-level tasks expressed in natural language into a sequence of executable actions.
Map-based Modular Approach for Zero-shot Embodied Question Answering
Embodied Question Answering (EQA) serves as a benchmark task to evaluate the capability of robots to navigate within novel environments and identify objects in response to human queries.