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Found 9 papers, 1 papers with code
Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration
Specifically, DaTAPlan planner computes actions for an agent and a human to collaboratively and jointly achieve the tasks anticipated by the LLM, and the agent automatically adapts to unexpected changes in human action outcomes and preferences.
Sequence-Agnostic Multi-Object Navigation
The Multi-Object Navigation (MultiON) task requires a robot to localize an instance (each) of multiple object classes.
Object Goal Navigation Based on Semantics and RGB Ego View
This paper presents an architecture and methodology to empower a service robot to navigate an indoor environment with semantic decision making, given RGB ego view.
Spatial Relation Graph and Graph Convolutional Network for Object Goal Navigation
This paper describes a framework for the object-goal navigation task, which requires a robot to find and move to the closest instance of a target object class from a random starting position.
Object Goal Navigation using Data Regularized Q-Learning
Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal ('where to go') based on the semantic map to locate the target object instance.
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.
Interpretable Feature Recommendation for Signal Analytics
The proposed approach is based on Wide Learning architecture and provides means for interpretation of the recommended features.
Automation of Feature Engineering for IoT Analytics
Several pattern recognition principles and state of art (SoA) ML techniques are followed to design the overall approach for the proposed automation.
Towards Wide Learning: Experiments in Healthcare
In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline.
