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Found 5 papers, 0 papers with code
A Framework for Realistic Simulation of Daily Human Activity
For social robots like Astro which interact with and adapt to the daily movements of users within the home, realistic simulation of human activity is needed for feature development and testing.
Improved Inference of Human Intent by Combining Plan Recognition and Language Feedback
We propose a novel framework for plan and goal recognition in partially observable domains -- Dialogue for Goal Recognition (D4GR) enabling a robot to rectify its belief in human progress by asking clarification questions about noisy sensor data and sub-optimal human actions.
Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments
We propose Lang2LTL, a modular system and a software package that leverages large language models (LLMs) to ground temporal navigational commands to LTL specifications in environments without prior language data.
CAPE: Corrective Actions from Precondition Errors using Large Language Models
Our improvements transfer to a Boston Dynamics Spot robot initialized with a set of skills (specified in language) and associated preconditions, where CAPE improves the correctness metric of the executed task plans by 76. 49% compared to SayCan.
Where were my keys? -- Aggregating Spatial-Temporal Instances of Objects for Efficient Retrieval over Long Periods of Time
Robots equipped with situational awareness can help humans efficiently find their lost objects by leveraging spatial and temporal structure.
