mlsanityData to 14 Oct 2026

Low-cost Real-world Implementation of the Swing-up Pendulum for Deep Reinforcement Learning Experiments

arXiv 2503.11065 ↗First posted 14 Mar 2025cs.LG cs.AI cs.ROcited by 0

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

Abstract (arXiv)

Deep reinforcement learning (DRL) has had success in virtual and simulated domains, but due to key differences between simulated and real-world environments, DRL-trained policies have had limited success in real-world applications. To assist researchers to bridge the textitsim-to-real gap, in this paper, we describe a low-cost physical inverted pendulum apparatus and software environment for exploring sim-to-real DRL methods. In particular, the design of our apparatus enables detailed examination of the delays that arise in physical systems when sensing, communicating, learning, inferring and actuating. Moreover, we wish to improve access to educational systems, so our apparatus uses readily available materials and parts to reduce cost and logistical barriers. Our design shows how commercial, off-the-shelf electronics and electromechanical and sensor systems, combined with common metal extrusions, dowel and 3D printed couplings provide a pathway for affordable physical DRL apparatus. The physical apparatus is complemented with a simulated environment implemented using a high-fidelity physics engine and OpenAI Gym interface.

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 13 references that are in the map. First posted on arXiv 14 Mar 2025.

Builds on

8 of 13 references.

References · 8

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2018DeepMind Control Suitecited by 956
  5. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  6. 2019Challenges of Real-World Reinforcement Learningcited by 291
  7. 2018Unity: A General Platform for Intelligent Agentscited by 236
  8. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 213

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  1. 2024An Efficient Learning Control Framework With Sim-to-Real for String-Type Artificial Muscle-Driven Robotic Systems
  2. 2024Technical Report on Reinforcement Learning Control on the Lucas-N"ulle Inverted Pendulum
  3. 2025Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition
  4. 2025Training Directional Locomotion for Quadrupedal Low-Cost Robotic Systems via Deep Reinforcement Learning
  5. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
  6. 2026Physical Atari: A Robust and Accessible Platform for Real-time Reinforcement Learning on Robots
  7. 2026Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics
  8. 2025Sim-to-Real Transfer in Deep Reinforcement Learning for Bipedal Locomotion