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Found 9 papers, 3 papers with code
Multimodal Coherent Explanation Generation of Robot Failures
The explainability of a robot's actions is crucial to its acceptance in social spaces.
tagE: Enabling an Embodied Agent to Understand Human Instructions
At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language.
Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?
In this work, we present a method to incorporate a robot's visual information into an ASR system and improve the recognition of a spoken utterance containing a visible entity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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DoRO: Disambiguation of referred object for embodied agents
Given an area where the intended object is, DoRO finds all the instances of the object by aggregating observations from multiple views while exploring & scanning the area.
Talk-to-Resolve: Combining scene understanding and spatial dialogue to resolve granular task ambiguity for a collocated robot
The utility of collocating robots largely depends on the easy and intuitive interaction mechanism with the human.
Detecting socially interacting groups using f-formation: A survey of taxonomy, methods, datasets, applications, challenges, and future research directions
As a result, social behavior is one of the most sought-after qualities that a robot can possess.
Let me join you! Real-time F-formation recognition by a socially aware robot
Our proposed pipeline consists of -- a) a skeletal key points estimator (a total of 17) for the detected human in the scene, b) a learning model (using a feature vector based on the skeletal points) using CRF to detect groups of people and outlier person in a scene, and c) a separate learning model using a multi-class Support Vector Machine (SVM) to predict the exact F-formation of the group of people in the current scene and the angle of approach for the viewing robot.
Enabling human-like task identification from natural conversation
In this work, we provide a non-trivial method to combine an NLP engine and a planner such that a robot can successfully identify tasks and all the relevant parameters and generate an accurate plan for the task.
DeComplex: Task planning from complex natural instructions by a collocating robot
However, it is not trivial to execute the human intended tasks as natural language expressions can have large linguistic variations.
