Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems
- URL: http://arxiv.org/abs/2404.06413v2
- Date: Mon, 16 Sep 2024 22:05:56 GMT
- Title: Foundation Models to the Rescue: Deadlock Resolution in Connected Multi-Robot Systems
- Authors: Kunal Garg, Songyuan Zhang, Jacob Arkin, Chuchu Fan,
- Abstract summary: Connected multi-agent robotic systems (MRS) are prone to deadlocks in an obstacle environment.
This paper explores the possibility of using text-based models, i.e., large language models (LLMs), and text-and-image-based models (VLMs), as high-level planners for deadlock resolution.
We propose a hierarchical control framework where a foundation model-based high-level planner helps to resolve deadlocks by assigning a leader to the MRS along with a set of waypoints for the MRS leader.
- Score: 11.012092202226855
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Connected multi-agent robotic systems (MRS) are prone to deadlocks in an obstacle environment where the robots can get stuck away from their desired locations under a smooth low-level control policy. Without an external intervention, often in terms of a high-level command, a low-level control policy cannot resolve such deadlocks. Utilizing the generalizability and low data requirements of foundation models, this paper explores the possibility of using text-based models, i.e., large language models (LLMs), and text-and-image-based models, i.e., vision-language models (VLMs), as high-level planners for deadlock resolution. We propose a hierarchical control framework where a foundation model-based high-level planner helps to resolve deadlocks by assigning a leader to the MRS along with a set of waypoints for the MRS leader. Then, a low-level distributed control policy based on graph neural networks is executed to safely follow these waypoints, thereby evading the deadlock. We conduct extensive experiments on various MRS environments using the best available pre-trained LLMs and VLMs. We compare their performance with a graph-based planner in terms of effectiveness in helping the MRS reach their target locations and computational time. Our results illustrate that, compared to grid-based planners, the foundation models perform better in terms of the goal-reaching rate and computational time for complex environments, which helps us conclude that foundation models can assist MRS operating in complex obstacle-cluttered environments to resolve deadlocks efficiently.
Related papers
- Scaling Autonomous Agents via Automatic Reward Modeling And Planning [52.39395405893965]
Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks.
However, they still struggle with problems requiring multi-step decision-making and environmental feedback.
We propose a framework that can automatically learn a reward model from the environment without human annotations.
arXiv Detail & Related papers (2025-02-17T18:49:25Z) - Learning Efficient Flocking Control based on Gibbs Random Fields [8.715391538937707]
Multi-agent reinforcement learning framework built on Gibbs Random Fields (GRFs)
An action attention module is introduced to implicitly anticipate the motion intentions of neighboring robots.
Proposed framework enables learning an efficient distributed control policy for multi-robot systems in challenging environments with success rate around $99%$.
arXiv Detail & Related papers (2025-02-05T08:27:58Z) - Solving Finite-Horizon MDPs via Low-Rank Tensors [9.072279909866845]
We study the problem of learning optimal policies in finite-horizon Markov Decision Processes (MDPs)
In finite-horizon MDPs, the policies, and therefore the value functions (VFs) are not stationary.
We propose modeling the VFs of finite-horizon MDPs as low-rank tensors, enabling a scalable representation that renders the problem of learning optimal policies tractable.
arXiv Detail & Related papers (2025-01-17T23:10:50Z) - MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [52.739500459903724]
Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation.
We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents.
We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
arXiv Detail & Related papers (2024-11-26T17:53:44Z) - Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data.
For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
arXiv Detail & Related papers (2023-11-23T15:46:54Z) - Layered controller synthesis for dynamic multi-agent systems [0.0]
We present a layered approach for multi-agent control problem, decomposed into three stages.
We use SWA-SMT solutions as the initial training dataset for our last stage, which aims at obtaining a neural network control policy.
arXiv Detail & Related papers (2023-07-13T13:56:27Z) - Evaluating model-based planning and planner amortization for continuous
control [79.49319308600228]
We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning.
We find that well-tuned model-free agents are strong baselines even for high DoF control problems.
We show that it is possible to distil a model-based planner into a policy that amortizes the planning without any loss of performance.
arXiv Detail & Related papers (2021-10-07T12:00:40Z) - Modular Deep Reinforcement Learning for Continuous Motion Planning with
Temporal Logic [59.94347858883343]
This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP)
The novelty is to design an embedded product MDP (EP-MDP) between the LDGBA and the MDP.
The proposed LDGBA-based reward shaping and discounting schemes for the model-free reinforcement learning (RL) only depend on the EP-MDP states.
arXiv Detail & Related papers (2021-02-24T01:11:25Z) - Learning High-Level Policies for Model Predictive Control [54.00297896763184]
Model Predictive Control (MPC) provides robust solutions to robot control tasks.
We propose a self-supervised learning algorithm for learning a neural network high-level policy.
We show that our approach can handle situations that are difficult for standard MPC.
arXiv Detail & Related papers (2020-07-20T17:12:34Z) - From proprioception to long-horizon planning in novel environments: A
hierarchical RL model [4.44317046648898]
In this work, we introduce a simple, three-level hierarchical architecture that reflects different types of reasoning.
We apply our method to a series of navigation tasks in the Mujoco Ant environment.
arXiv Detail & Related papers (2020-06-11T17:19:12Z)
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.