Navigation of micro-robot swarms for targeted delivery using reinforcement learning

• URL: http://arxiv.org/abs/2306.17598v1
• Date: Fri, 30 Jun 2023 12:17:39 GMT
• Title: Navigation of micro-robot swarms for targeted delivery using reinforcement learning
• Authors: Akshatha Jagadish, Manoj Varma
• Abstract summary: We use the Reinforcement Learning (RL) algorithms Proximal Policy Optimization (PPO) and Robust Policy Optimization (RPO) to navigate a swarm of 4, 9 and 16 microswimmers. We look at both PPO and RPO performances with limited state information scenarios and also test their robustness for random target location and size.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Micro robotics is quickly emerging to be a promising technological solution to many medical treatments with focus on targeted drug delivery. They are effective when working in swarms whose individual control is mostly infeasible owing to their minute size. Controlling a number of robots with a single controller is thus important and artificial intelligence can help us perform this task successfully. In this work, we use the Reinforcement Learning (RL) algorithms Proximal Policy Optimization (PPO) and Robust Policy Optimization (RPO) to navigate a swarm of 4, 9 and 16 microswimmers under hydrodynamic effects, controlled by their orientation, towards a circular absorbing target. We look at both PPO and RPO performances with limited state information scenarios and also test their robustness for random target location and size. We use curriculum learning to improve upon the performance and demonstrate the same in learning to navigate a swarm of 25 swimmers and steering the swarm to exemplify the manoeuvring capabilities of the RL model.

Related papers

• Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation [0.6554326244334868]
  This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep neural network to generate control signals guiding it toward a specified target while avoiding obstacles.
  arXiv  Detail & Related papers  (2024-05-25T15:08:36Z)
• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [85.21378553454672]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment. We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• Training microrobots to swim by a large language model [0.0]
  We develop a minimal, unified prompt composed of only five sentences. The same prompt successfully guides two distinct articulated microrobots in mastering their signature strokes. Remarkably, our LLM-based decision-making strategy substantially surpasses a traditional reinforcement learning method in terms of training speed.
  arXiv  Detail & Related papers  (2024-01-21T12:18:59Z)
• Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications [11.812602599752294]
  We consider robots with unknown dynamics operating in environments with unknown structure. Our goal is to synthesize a control policy that maximizes the probability of satisfying an automaton-encoded task. We propose a novel DRL algorithm, which has the capability to learn control policies at a notably faster rate compared to similar methods.
  arXiv  Detail & Related papers  (2023-11-28T18:59:58Z)
• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning [66.10854214036605]
  A central question in robotics is how to design a control system for an agile mobile robot. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour.
  arXiv  Detail & Related papers  (2023-10-17T02:40:27Z)
• Robot Fleet Learning via Policy Merging [58.5086287737653]
  We propose FLEET-MERGE to efficiently merge policies in the fleet setting. We show that FLEET-MERGE consolidates the behavior of policies trained on 50 tasks in the Meta-World environment. We introduce a novel robotic tool-use benchmark, FLEET-TOOLS, for fleet policy learning in compositional and contact-rich robot manipulation tasks.
  arXiv  Detail & Related papers  (2023-10-02T17:23:51Z)
• Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation [72.24964965882783]
  Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error. Real-world robotic tasks often suffer from sparse rewards, leading to inefficient exploration and suboptimal policies. We introduce Confidence-Controlled Exploration (CCE), a novel method that improves sample efficiency in RL-based robotic navigation without modifying the reward function.
  arXiv  Detail & Related papers  (2023-06-09T18:45:15Z)
• Chemotaxis of sea urchin sperm cells through deep reinforcement learning [0.0]
  In this work, we investigate how a model of sea urchin sperm cell can self-learn chemotactic motion in a chemoattractant concentration field. We employ an artificial neural network to act as a decision-making agent and facilitate the sperm cell to discover efficient maneuver strategies. Our results provide insights to the chemotactic process of sea urchin sperm cells and also prepare guidance for the intelligent maneuver of microrobots.
  arXiv  Detail & Related papers  (2022-08-02T06:04:32Z)
• Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning [0.0]
  Deep reinforcement learning is a promising method of autonomously developing robust controllers for creating smart microrobots. Here, we report the development of a smart helical magnetic hydrogel microrobot that used the soft actor critic reinforcement learning algorithm to autonomously derive a control policy. The reinforcement learning agent learned successful control policies with fewer than 100,000 training steps, demonstrating sample efficiency for fast learning.
  arXiv  Detail & Related papers  (2022-01-14T18:42:18Z)
• A Framework for Efficient Robotic Manipulation [79.10407063260473]
  We show that a single robotic arm can learn sparse-reward manipulation policies from pixels. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels.
  arXiv  Detail & Related papers  (2020-12-14T22:18:39Z)
• Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning [91.13113161754022]
  We introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop.
  arXiv  Detail & Related papers  (2020-02-20T21:39:25Z)

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.