State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey

• URL: http://arxiv.org/abs/2408.11822v1
• Date: Sat, 3 Aug 2024 21:22:08 GMT
• Title: State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey
• Authors: Bin Wu, C Steve Suh,
• Abstract summary: Multi-robot systems (MRS) built on this foundation are undergoing drastic evolution. The fusion of artificial intelligence technology with robot hardware is seeing broad application possibilities for MRS. This article surveys the state-of-the-art of robot learning in the context of Multi-Robot Cooperation.
• Score: 2.686336957004475
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the continuous breakthroughs in core technology, the dawn of large-scale integration of robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on this foundation are undergoing drastic evolution. The fusion of artificial intelligence technology with robot hardware is seeing broad application possibilities for MRS. This article surveys the state-of-the-art of robot learning in the context of Multi-Robot Cooperation (MRC) of recent. Commonly adopted robot learning methods (or frameworks) that are inspired by humans and animals are reviewed and their advantages and disadvantages are discussed along with the associated technical challenges. The potential trends of robot learning and MRS integration exploiting the merging of these methods with real-world applications is also discussed at length. Specifically statistical methods are used to quantitatively corroborate the ideas elaborated in the article.

Related papers

• A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics [53.33976793493801]
  We organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We focus on practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging.
  arXiv  Detail & Related papers  (2024-11-08T17:20:47Z)
• $π_0$: A Vision-Language-Action Flow Model for General Robot Control [77.32743739202543]
  We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people, and its ability to acquire new skills via fine-tuning.
  arXiv  Detail & Related papers  (2024-10-31T17:22:30Z)
• Grounding Robot Policies with Visuomotor Language Guidance [15.774237279917594]
  We propose an agent-based framework for grounding robot policies to the current context. The proposed framework is composed of a set of conversational agents designed for specific roles. We demonstrate that our approach can effectively guide manipulation policies to achieve significantly higher success rates.
  arXiv  Detail & Related papers  (2024-10-09T02:00:37Z)
• Generalized Robot Learning Framework [10.03174544844559]
  We present a low-cost robot learning framework that is both easily reproducible and transferable to various robots and environments. We demonstrate that deployable imitation learning can be successfully applied even to industrial-grade robots.
  arXiv  Detail & Related papers  (2024-09-18T15:34:31Z)
• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Hybrid ASR for Resource-Constrained Robots: HMM - Deep Learning Fusion [0.0]
  This paper presents a novel hybrid Automatic Speech Recognition (ASR) system designed specifically for resource-constrained robots. The proposed approach combines Hidden Markov Models (HMMs) with deep learning models and leverages socket programming to distribute processing tasks effectively. In this architecture, the HMM-based processing takes place within the robot, while a separate PC handles the deep learning model.
  arXiv  Detail & Related papers  (2023-09-11T15:28:19Z)
• Distributed Reinforcement Learning for Robot Teams: A Review [10.92709534981466]
  Recent advances in sensing, actuation, and computation have opened the door to multi-robot systems. Community has leveraged model-free multi-agent reinforcement learning to devise efficient, scalable controllers for multi-robot systems. Recent findings: Decentralized MRS face fundamental challenges, such as non-stationarity and partial observability.
  arXiv  Detail & Related papers  (2022-04-07T15:34:19Z)
• Dual-Arm Adversarial Robot Learning [0.6091702876917281]
  We propose dual-arm settings as platforms for robot learning. We will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.
  arXiv  Detail & Related papers  (2021-10-15T12:51:57Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
  Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems. We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
  arXiv  Detail & Related papers  (2021-09-19T18:00:51Z)

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.