Deterministic Policy Gradient Based Robotic Path Planning with Continuous Action Spaces

Paul, Somdyuti; Vig, Lovekesh

doi:10.1109/iccvw.2017.91

Cited by 5 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, the DDPG [8] algorithm is also widely used in robot control. By utilizing two 2D views of the environment rather than a depth image, Paul et al [34] proposed a DDPG-based framework to implement the path planning for the random target reach. Do et al [35] presented an approach to learn to pour water for a robot using DDPG and successfully transferred the learned policy in simulation to the real robot.…”

Section: Drl In Robot Controlmentioning

confidence: 99%

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Liu

Xiong

et al. 2021

Advanced Engineering Informatics

View full text Add to dashboard Cite

Aiming at human-robot collaboration in manufacturing, the operator's safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human-robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.

show abstract

Section: Drl In Robot Controlmentioning

confidence: 99%

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Liu

Xiong

et al. 2021

Advanced Engineering Informatics

View full text Add to dashboard Cite

show abstract

“…In [29,30], robot path planning methods are proposed using a deep Q-network algorithm with emphasis on learning efficiency. For path training, a stereo image is used to train DDPG (Deep Deterministic Policy Gradient) in [31]. In [32], a real robot is trained using reinforcement learning for its path planning.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning of Robot Manipulators for a Smoother Path Using a Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay

et al. 2020

View full text Add to dashboard Cite

In order to enhance performance of robot systems in the manufacturing industry, it is essential to develop motion and task planning algorithms. Especially, it is important for the motion plan to be generated automatically in order to deal with various working environments. Although PRM (Probabilistic Roadmap) provides feasible paths when the starting and goal positions of a robot manipulator are given, the path might not be smooth enough, which can lead to inefficient performance of the robot system. This paper proposes a motion planning algorithm for robot manipulators using a twin delayed deep deterministic policy gradient (TD3) which is a reinforcement learning algorithm tailored to MDP with continuous action. Besides, hindsight experience replay (HER) is employed in the TD3 to enhance sample efficiency. Since path planning for a robot manipulator is an MDP (Markov Decision Process) with sparse reward and HER can deal with such a problem, this paper proposes a motion planning algorithm using TD3 with HER. The proposed algorithm is applied to 2-DOF and 3-DOF manipulators and it is shown that the designed paths are smoother and shorter than those designed by PRM.

show abstract

UGV Navigation Optimization Aided by Reinforcement Learning-Based Path Tracking

et al. 2018

View full text Add to dashboard Cite

Deterministic Policy Gradient Based Robotic Path Planning with Continuous Action Spaces

Cited by 5 publications

References 12 publications

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Motion Planning of Robot Manipulators for a Smoother Path Using a Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay

UGV Navigation Optimization Aided by Reinforcement Learning-Based Path Tracking

Contact Info

Product

Resources

About