Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs

• URL: http://arxiv.org/abs/2409.10692v1
• Date: Mon, 16 Sep 2024 19:39:52 GMT
• Title: Encoding Reusable Multi-Robot Planning Strategies as Abstract Hypergraphs
• Authors: Khen Elimelech, James Motes, Marco Morales, Nancy M. Amato, Moshe Y. Vardi, Lydia E. Kavraki,
• Abstract summary: Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. To accelerate MR-TP over a system's lifetime, this work looks at combining two recent advances.
• Score: 27.791001793093805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially with the number of robots and task complexity, making them challenging for online solution. To accelerate MR-TP over a system's lifetime, this work looks at combining two recent advances: (i) Decomposable State Space Hypergraph (DaSH), a novel hypergraph-based framework to efficiently model and solve MR-TP problems; and \mbox{(ii) learning-by-abstraction,} a technique that enables automatic extraction of generalizable planning strategies from individual planning experiences for later reuse. Specifically, we wish to extend this strategy-learning technique, originally designed for single-robot planning, to benefit multi-robot planning using hypergraph-based MR-TP.

Related papers

• Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning [17.989467671223043]
  We construct an efficient multi-stage HRL-based multi-robot task planner for hyper scale MRTP in RMFS. To ensure optimality, the planner is designed with a centralized architecture, but it also brings the challenges of scaling up and generalization. Our planner can successfully scale up to hyper scale MRTP instances in RMFS with up to 200 robots and 1000 retrieval racks on unlearned maps.
  arXiv  Detail & Related papers  (2024-12-27T09:07:11Z)
• SCoTT: Wireless-Aware Path Planning with Vision Language Models and Strategic Chains-of-Thought [78.53885607559958]
  A novel approach using vision language models (VLMs) is proposed for enabling path planning in complex wireless-aware environments. To this end, insights from a digital twin with real-world wireless ray tracing data are explored. Results show that SCoTT achieves very close average path gains compared to DP-WA* while at the same time yielding consistently shorter path lengths.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
  COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems. A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
  arXiv  Detail & Related papers  (2024-09-23T15:53:41Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning [61.294110816231886]
  We introduce a sparse, reusable, and flexible policy, Sparse Diffusion Policy (SDP) SDP selectively activates experts and skills, enabling efficient and task-specific learning without retraining the entire model. Demos and codes can be found in https://forrest-110.io/sparse_diffusion_policy/.
  arXiv  Detail & Related papers  (2024-07-01T17:59:56Z)
• Optimal Integrated Task and Path Planning and Its Application to Multi-Robot Pickup and Delivery [10.530860023128406]
  We propose a generic multi-robot planning mechanism that combines an optimal task planner and an optimal path planner. The Integrated planner, through the interaction of the task planner and the path planner, produces optimal collision-free trajectories for the robots.
  arXiv  Detail & Related papers  (2024-03-02T17:48:40Z)
• Decomposition-based Hierarchical Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications [9.150196865878234]
  We formulate a decomposition-based hierarchical framework for robotic planning with temporal logic specifications. A Mixed Linear Program is used to assign sub-tasks to various robots. Our approach was experimentally applied to domains of navigation and manipulation.
  arXiv  Detail & Related papers  (2023-08-20T23:53:13Z)
• AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation [50.737355245505334]
  We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks. The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation.
  arXiv  Detail & Related papers  (2023-05-30T09:54:20Z)
• Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications [59.34642007625687]
  The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots. An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
  arXiv  Detail & Related papers  (2022-05-03T17:14:47Z)
• Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning [27.384742641275228]
  We present a new approach for bootstrapping the entire hierarchical planning process. It shows how abstract states and actions for new environments can be computed automatically. It uses the learned abstractions in a novel multi-source bi-directional hierarchical robot planning algorithm.
  arXiv  Detail & Related papers  (2022-02-02T08:11:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.