# Robotic Arm Control and Task Training through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2005.02632 · HTML: https://mlsanity.com/paper/2005.02632/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 6 of 6)

- [Rethinking Adversarial Attacks in Reinforcement Learning from Policy Distribution Perspective](https://mlsanity.com/paper/2501.03562/) (2025)
- [Analyzing and Bridging the Gap between Maximizing Total Reward and Discounted Reward in Deep Reinforcement Learning](https://mlsanity.com/paper/2407.13279/) (2024)
- [Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm](https://mlsanity.com/paper/2308.14652/) (2023)
- [Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing](https://mlsanity.com/paper/2502.19340/) (2025)
- [Robotic Skill Diversification via Active Mutation of Reward Functions in Reinforcement Learning During a Liquid Pouring Task](https://mlsanity.com/paper/2509.18463/) (2025)
- [Hardware-Software Collaborative Computing of Photonic Spiking Reinforcement Learning for Robotic Continuous Control](https://mlsanity.com/paper/2512.00427/) (2025)

## Abstract (arXiv)

This paper proposes a detailed and extensive comparison of the Trust Region Policy Optimization and DeepQ-Network with Normalized Advantage Functions with respect to other state of the art algorithms, namely Deep Deterministic Policy Gradient and Vanilla Policy Gradient. Comparisons demonstrate that the former have better performances then the latter when asking robotic arms to accomplish manipulation tasks such as reaching a random target pose and pick &placing an object. Both simulated and real-world experiments are provided. Simulation lets us show the procedures that we adopted to precisely estimate the algorithms hyper-parameters and to correctly design good policies. Real-world experiments let show that our polices, if correctly trained on simulation, can be transferred and executed in a real environment with almost no changes.