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Anticipate & Collab: Data-driven Task Anticipation and Knowledge-driven Planning for Human-robot Collaboration

arXiv 2404.03587 ↗First posted 4 Apr 2024cs.RO cs.AIcited by 0

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Abstract (arXiv)

An agent assisting humans in daily living activities can collaborate more effectively by anticipating upcoming tasks. Data-driven methods represent the state of the art in task anticipation, planning, and related problems, but these methods are resource-hungry and opaque. Our prior work introduced a proof of concept framework that used an LLM to anticipate 3 high-level tasks that served as goals for a classical planning system that computed a sequence of low-level actions for the agent to achieve these goals. This paper describes DaTAPlan, our framework that significantly extends our prior work toward 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. We evaluate DaTAPlan capabilities in a realistic simulation environment, demonstrating accurate task anticipation, effective human-robot collaboration, and the ability to adapt to unexpected changes. Project website: https://dataplan-hrc.github.io

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 36 references that are in the map. First posted on arXiv 4 Apr 2024.

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References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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  6. 2023Large Language Models as Commonsense Knowledge for Large-Scale Task Planningcited by 231
  7. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planningcited by 207
  8. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
  9. 2023Translating Natural Language to Planning Goals with Large-Language Modelscited by 133
  10. 2014The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarkscited by 87
  11. 2024AutoGPT+P: Affordance-based Task Planning with Large Language Modelscited by 19
  12. 2022Intuitive and Efficient Human-robot Collaboration via Real-time Approximate Bayesian Inferencecited by 5
1 more
  1. 2024What's the Plan? Evaluating and Developing Planning-Aware Techniques for Language Modelscited by 4

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