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Papers on “reinforcement learning robotics control”

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1. Continuous control for robot based on deep reinforcement learning

   Shansi Zhang · 2019 · 951 cites

   results of redundant manipulator . . . . . . . .

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2. Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning

   Lukas Brunke, Melissa Greeff, Adam W. Hall, et al. · 2022 · Annual Review of Control Robotics and Autonomous Systems · 783 cites

   The last half decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision-making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. It includes learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods that can formally certify the safet

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3. Residual Reinforcement Learning for Robot Control

   Tobias Johannink, Shikhar Bahl, Ashvin Nair, et al. · 2019 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · 477 cites

   Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. Hence, applying control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment. Reinforcement learning (RL) methods have been demonstrated to be capable of learning continuous robot controllers from interactions with the environment, even for problems that i

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4. Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots

   Zhongyu Li, Xuxin Cheng, Xue Bin Peng, et al. · 2021 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · 311 cites

   Developing robust walking controllers for bipedal robots is a challenging endeavor. Traditional model-based locomotion controllers require simplifying assumptions and careful modelling; any small errors can result in unstable control. To address these challenges for bipedal locomotion, we present a model-free reinforcement learning framework for training robust locomotion policies in simulation, which can then be transferred to a real bipedal Cassie robot. To facilitate sim-to-real transfer, domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. The learned policies enable Cassie to perform a set of diverse and dynamic

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5. Reinforcement learning for versatile, dynamic, and robust bipedal locomotion control

   Zhongyu Li, Xue-Bin Peng, P. Abbeel, et al. · 2024 · The International Journal of Robotics Research · 269 cites

   This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal robots. Going beyond focusing on a single locomotion skill, we develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. Our RL-based controller incorporates a novel dual-history architecture, utilizing both a long-term and short-term input/output (I/O) history of the robot. This control architecture, when trained through the proposed end-to-end RL approach, consistently outperforms other methods across a diverse range of skills in both simulation and the r

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6. Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review

   Rongrong Liu, Florent P. Nageotte, Philippe Zanne, et al. · 2021 · Robotics · 205 cites

   Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named reinforcement learning, tries to find an optimal behavior strategy through interactions with the environment. Combining deep learning and reinforcement learning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals, such as robotic manipulation and control tasks, that neither method permits resolving when applied on its own. In this p

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7. Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis

   Yue Wen, Jennie Si, Andrea Brandt, et al. · 2019 · IEEE Transactions on Cybernetics · 181 cites

   Robotic prostheses deliver greater function than passive prostheses, but we face the challenge of tuning a large number of control parameters in order to personalize the device for individual amputee users. This problem is not easily solved by traditional control designs or the latest robotic technology. Reinforcement learning (RL) is naturally appealing. The recent, unprecedented success of AlphaZero demonstrated RL as a feasible, large-scale problem solver. However, the prosthesis-tuning problem is associated with several unaddressed issues such as that it does not have a known and stable model, the continuous states and controls of the problem may result in a curse of dimensionality, and

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8. Improving Vision-Language-Action Model with Online Reinforcement Learning

   Yanjiang Guo, Jianke Zhang, Xiao-Yu Chen, et al. · 2025 · 2025 IEEE International Conference on Robotics and Automation (ICRA) · 141 cites

   Recent studies have successfully integrated large vision-language models (VLMs) into low-level robotic control by supervised fine-tuning (SFT) with expert robotic datasets, resulting in what we term vision-language-action (VLA) models. Although the VLA models are powerful, how to improve these large models during interaction with environments remains an open question. In this paper, we explore how to further improve these VLA models via Reinforcement Learning (RL), a commonly used fine-tuning technique for large models. However, we find that directly applying online RL to large VLA models presents significant challenges, including training instability that severely impacts the performance of

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