mlsanityData to 14 Oct 2026

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators

arXiv 2509.17381 ↗First posted 22 Sep 2025cs.ROcited by 0

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

Abstract (arXiv)

Generating obstacle-free trajectories for robotic manipulators in unstructured and cluttered environments remains a significant challenge. Existing motion planning methods often require additional computational effort to generate the final trajectory by solving kinematic or dynamic equations. This paper highlights the strong potential of model-free reinforcement learning methods over model-based approaches for obstacle-free trajectory planning in joint space. We propose a fast trajectory planning system for manipulators that combines vision-based path planning in task space with reinforcement learning-based obstacle avoidance in joint space. We divide the framework into two key components. The first introduces an innovative vision-based trajectory planner in task space, leveraging the large-scale fast segment anything (FSA) model in conjunction with basis spline (B-spline)-optimized kinodynamic path searching. The second component enhances the proximal policy optimization (PPO) algorithm by integrating action ensembles (AE) and policy feedback (PF), which greatly improve precision and stability in goal-reaching and obstacle avoidance within the joint space. These PPO enhancements increase the algorithm's adaptability across diverse robotic tasks, ensuring consistent execution of commands from the first component by the manipulator, while also enhancing both obstacle avoidance efficiency and reaching accuracy. The experimental results demonstrate the effectiveness of PPO enhancements, as well as simulation-to-simulation (Sim-to-Sim) and simulation-to-reality (Sim-to-Real) transfer, in improving model robustness and planner efficiency in complex scenarios. These enhancements allow the robot to perform obstacle avoidance and real-time trajectory planning in obstructed environments. Project page available at: https://sites.google.com/view/ftp4rm/home

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 20 of its 38 references that are in the map. First posted on arXiv 22 Sep 2025.

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20 of 38 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024TraKDis: A Transformer-based Knowledge Distillation Approach for Visual Reinforcement Learning with Application to Cloth Manipulation24 Jan

Other references · 19

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  5. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
  6. 2021Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemblecited by 302
  7. 2023Fast Segment Anythingcited by 290
  8. 2021Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemblecited by 214
  9. 2022Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matterscited by 76
  10. 2019Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotorscited by 45
  11. 2019Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flightcited by 41
  12. 2020RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flightcited by 23
7 more
  1. 2020Learning Vision-based Reactive Policies for Obstacle Avoidancecited by 12
  2. 2023Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformerscited by 8
  3. 2020Joint Space Control via Deep Reinforcement Learningcited by 7
  4. 2019Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Pathscited by 4
  5. 2024Reflective Policy Optimizationcited by 3
  6. 2024Neural network modelling of kinematic and dynamic features for signature verificationcited by 3
  7. 2024Design, Control, and Motion-Planning for a Root-Perching Rotor-Distributed Manipulatorcited by 1

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  1. 2026Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators ↗
  2. 2025URPlanner: A Universal Paradigm For Collision-Free Robotic Motion Planning Based on Deep Reinforcement Learning
  3. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
  4. 2020Fast Adaptation of Manipulator Trajectories to Task Perturbation By Differentiating through the Optimal Solution ↗
  5. 2024Self-Supervised Learning-Based Path Planning and Obstacle Avoidance Using PPO and B-Splines in Unknown Environments
  6. 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning
  7. 2024Collision-Free Robot Navigation in Crowded Environments using Learning based Convex Model Predictive Control
  8. 2026Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation