mlsanityData to 14 Oct 2026

Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL

arXiv 2403.16652 ↗First posted 25 Mar 2024cs.RO cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

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.

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.

Builds on

8 of 14 references.

References · 8

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2017Hindsight Experience Replaycited by 1,136
  3. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 260
  4. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 157
  5. 2020Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphscited by 30
  6. 2023Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearrangingcited by 8
  7. 2020Complex Robotic Manipulation via Graph-Based Hindsight Goal Generationcited by 7
  8. 2021Multi-Agent Path Planning based on MPC and DDPGcited by 1

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  1. 2020Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks with Base Controllers
  2. 2024Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning
  3. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
  4. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
  5. 2024Deep Reinforcement Learning for Mobile Robot Path Planning
  6. 2025A Reinforcement Learning Approach to Non-prehensile Manipulation through Sliding
  7. 2025Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing
  8. 2024Combining Deep Reinforcement Learning with a Jerk-Bounded Trajectory Generator for Kinematically Constrained Motion Planning