Work

EMPOWER: Embodied Multi-role Open-vocabulary Planning with Online Grounding and Execution

Year: 2024

Type: conference-paper

Abstract: Task planning for robots in real-life settings presents significant challenges. These challenges stem from three primary issues: the difficulty in identifying grounded sequences of steps to achieve a goal; the lack of a standardized mapping between high-level actions and low-level commands; and the challenge of maintaining low computational overhead given the limited resources of robotic hardware. We introduce EMPOWER, a framework designed for open-vocabulary online grounding and planning for embodied agents aimed at addressing these issues. By leveraging efficient pre-trained foundation models and a multi-role mechanism, EMPOWER demonstrates notable improvements in grounded planning and execution. Quantitative results highlight the effectiveness of our approach, achieving an average success rate of 0.73 across six different real-life scenarios using a TIAGo robot.

Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI: 10.1109/iros58592.2024.10802251

Authors: Francesco Argenziano, Michele Brienza, Vincenzo Suriani, Daniele Nardi, Domenico D. Bloisi

Institutions: Sapienza University of Rome, University of Basilicata, Università degli Studi Internazionali di Roma

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Sapienza Università di Roma, NextGenerationEU

Award: G8779474850