# Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control

arXiv: https://arxiv.org/abs/1511.03791 · HTML: https://mlsanity.com/paper/1511.03791/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2015.

## Built on by (most cited first, 30 of 33)

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- [HypeRL: Hypernetwork-Based Reinforcement Learning for Control of Parametrized Dynamical Systems](https://mlsanity.com/paper/2501.04538/) (2025)
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- [Defenses Against Multi-Sticker Physical Domain Attacks on Classifiers](https://mlsanity.com/paper/2101.11060/) (2021)
- [End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation](https://mlsanity.com/paper/2202.06027/) (2022)
- [Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control](https://mlsanity.com/paper/2203.05985/) (2022)
- [Machine Learning Meets Advanced Robotic Manipulation](https://mlsanity.com/paper/2309.12560/) (2023)
- [DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future](https://mlsanity.com/paper/2105.13806/) (2021)
- [Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots](https://mlsanity.com/paper/2112.05299/) (2021)
- [An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.12114/) (2022)
- [Imitation Learning with Additional Constraints on Motion Style using Parametric Bias](https://mlsanity.com/paper/2407.08057/) (2024)
- [A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Perspectives](https://mlsanity.com/paper/2101.00829/) (2021)
- [Towards a Sample Efficient Reinforcement Learning Pipeline for Vision Based Robotics](https://mlsanity.com/paper/2105.09719/) (2021)
- [Machine Beats Machine: Machine Learning Models to Defend Against Adversarial Attacks](https://mlsanity.com/paper/2209.13963/) (2022)
- [GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning](https://mlsanity.com/paper/2409.06427/) (2024)
- [From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control](https://mlsanity.com/paper/2506.00043/) (2025)

## Abstract (arXiv)

This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.