# Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL

arXiv: https://arxiv.org/abs/2403.16652 · HTML: https://mlsanity.com/paper/2403.16652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 8 of 8)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research](https://mlsanity.com/paper/1802.09464/) (2018)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs](https://mlsanity.com/paper/2007.12640/) (2020)
- [Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging](https://mlsanity.com/paper/2302.10446/) (2023)
- [Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation](https://mlsanity.com/paper/2007.13486/) (2020)
- [Multi-Agent Path Planning based on MPC and DDPG](https://mlsanity.com/paper/2102.13283/) (2021)

## Abstract (arXiv)

This study is about the implementation of a reinforcement learning algorithm in the trajectory planning of manipulators. We have a 7-DOF robotic arm to pick and place the randomly placed block at a random target point in an unknown environment. The obstacle is randomly moving which creates a hurdle in picking the object. The objective of the robot is to avoid the obstacle and pick the block with constraints to a fixed timestamp. In this literature, we have applied a deep deterministic policy gradient (DDPG) algorithm and compared the model's efficiency with dense and sparse rewards.