Task planning News and Research

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LLM3: Bridging Symbolic Task Planning and Continuous Motion Generation with LLMs

In their paper submitted to arxiv, researchers introduced LLM3, a groundbreaking Task and Motion Planning (TAMP) framework that utilizes large language models (LLMs) to seamlessly integrate symbolic task planning and continuous motion generation. LLM3 leverages pre-trained LLMs to propose action sequences and generate action parameters iteratively, significantly reducing the need for domain-specific interfaces and manual effort.

21 Mar 2024

AutoGPT+P: Integrating Affordance-Based Scene Representation with LLMs for Robotic Task Planning

Researchers proposed AutoGPT+P, a novel system for robotic task planning that merges an affordance-based scene representation with large language models (LLMs). By combining visual data-based scene affordance extraction with LLMs' capabilities, AutoGPT+P enables robots to understand natural language commands, perceive their environment, and generate plans to fulfill user requests, even in scenarios where required objects are absent.

22 Feb 2024

Energy-Efficient UAV Cluster Missions through Enhanced Task Allocation and Trajectory Planning

Researchers present a comprehensive strategy for optimizing Unmanned Aerial Vehicle (UAV) cluster tasks in three-dimensional space, focusing on complete area coverage. The proposed approach incorporates an enhanced Fuzzy C-clustering algorithm for task allocation and introduces a Particle Swarm Hybrid Ant Colony (PSOHAC) algorithm for trajectory planning.

14 Nov 2023

Interactive Task Planning with Language Models

Researchers introduced an interactive robot framework that leverages Large Language Models (LLMs) to excel in long-term task planning, adapting to new goals and tasks during execution. The system seamlessly integrates high-level planning and low-level execution using language, demonstrating robustness and adaptability across tasks.

19 Oct 2023

ForceSight: Robust Mobile Manipulation Guided by Visual-Force Goals and Natural Language

Researchers at Georgia Tech have developed ForceSight, a system that combines deep neural networks and natural language instructions to enable robots to perform robust mobile manipulation tasks. ForceSight predicts visual-force goals and utilizes a vision transformer architecture to improve the accuracy of these predictions, significantly enhancing the robot's performance in various manipulation tasks.

26 Sep 2023

SayPlan: Scaling Up LLM-Based Task Planning for Robotics Using 3D Scene Graphs

Researchers propose SayPlan, a scalable approach for large-scale task planning in robotics using large language models (LLMs) grounded in three-dimensional scene graphs (3DSGs). The approach demonstrates high success rates in finding task-relevant subgraphs, reduces input tokens required for representation, and ensures near-perfect executability. While limitations exist, such as graph reasoning constraints and static object assumptions, the study paves the way for improved LLM-based planning in expansive environments.

16 Jul 2023