scholarly journals Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost

2019 ◽  
Author(s):  
Henry Zhu ◽  
Abhishek Gupta ◽  
Aravind Rajeswaran ◽  
Sergey Levine ◽  
Vikash Kumar
Keyword(s):  
Reinforcement Learning ◽  
Low Cost ◽  
Dexterous Manipulation

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Low Cost ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

AbstractWith the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

scholarly journals Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning

2021 ◽  
Author(s):  
Ari Viitala ◽  
Rinu Boney ◽  
Yi Zhao ◽  
Alexander Ilin ◽  
Juho Kannala
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Low Cost

scholarly journals Quadrotor Path Following and Reactive Obstacle Avoidance with Deep Reinforcement Learning

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Bartomeu Rubí ◽  
Bernardo Morcego ◽  
Ramon Pérez
Keyword(s):  
Reinforcement Learning ◽  
Obstacle Avoidance ◽  
Low Cost ◽  
Path Following ◽  
The State ◽  
Gradient Algorithm ◽  
Avoidance Task ◽  
Learning Approaches ◽  
Reward Function ◽  
Novel Structure

AbstractA deep reinforcement learning approach for solving the quadrotor path following and obstacle avoidance problem is proposed in this paper. The problem is solved with two agents: one for the path following task and another one for the obstacle avoidance task. A novel structure is proposed, where the action computed by the obstacle avoidance agent becomes the state of the path following agent. Compared to traditional deep reinforcement learning approaches, the proposed method allows to interpret the training process outcomes, is faster and can be safely trained on the real quadrotor. Both agents implement the Deep Deterministic Policy Gradient algorithm. The path following agent was developed in a previous work. The obstacle avoidance agent uses the information provided by a low-cost LIDAR to detect obstacles around the vehicle. Since LIDAR has a narrow field-of-view, an approach for providing the agent with a memory of the previously seen obstacles is developed. A detailed description of the process of defining the state vector, the reward function and the action of this agent is given. The agents are programmed in python/tensorflow and are trained and tested in the RotorS/gazebo platform. Simulations results prove the validity of the proposed approach.

scholarly journals A Low-Cost Compliant Gripper Using Cooperative Mini-Delta Robots for Dexterous Manipulation

2021 ◽  
Author(s):  
Pragna Mannam* ◽  
Avi Rudich* ◽  
Kevin Zhang* ◽  
Manuela Veloso ◽  
Oliver Kroemer ◽  
...  
Keyword(s):  
Low Cost ◽  
Dexterous Manipulation

scholarly journals SPF ICE: A Novel Approach to Predict the Optimal Amount of Silica to Preserve Glaciers Using Reinforcement Learning

2021 ◽  
Author(s):  
Aadhav Prabu
Keyword(s):  
Reinforcement Learning ◽  
Low Cost ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Levels ◽  
Optimal Amount ◽  
Novel Approach ◽  
The Pacific ◽  
Glacier Melting ◽  
Climate Crisis

<div><div><div><p>Glaciers cover nearly 10 percent of the earth’s surface but are melting at an inexorable rate. According to the pacific standard magazine, the Arctic sea ice has lost 80 percent of its volume since 1979. Antarctica’s ’Doomsday Glacier’ is melting faster and could raise global sea levels by two feet. As three-quarters of the earth’s freshwater is stored in glaciers, its melting depletes freshwater resources for millions of people. Glaciers also play a huge role in the climate crisis. Preserving glaciers is an important and imminent solution to save our planet. Silica microspheres are promising materials to prevent glacier melting as it reflects most of the sun’s radiation. When spread in layers over the glacier, it can slow the rate of melt and aid in new ice formation. However, if not used precisely, silica can be ineffective and expensive. SPF ICE is a novel method implemented to effectively de- termine the optimal amount of silica based on glacier’s properties to prevent its depletion substantially using reinforcement learning agents and a custom OpenAI Gym environment. The environment simulates a real-world model of a glacial setting using specific data, such as the glacier’s mass balance, tem- perature, and average accumulation and ablation. After testing the agents during many episodes, my solution reduced glacial melting by an average of 60.40% using the optimal amount of Silica. Additionally, this solution is customizable for any type of glacier. SPF ICE is an efficient and low-cost solution to curb glacier melting to preserve planet earth.</p></div></div></div>

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