2024/08/30 by Francesco Argenziano, Michele Brienza, Argenziano, Francesco +7 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Robotics (cs.RO) #Speech and dialogue systems
paper · pdf · doi:10.48550/arxiv.2408.17379
openalex publication_date 2024/08/30 · openalex created_date 2024/10/19 · openalex updated_date 2026/07/28
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.