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Papers on “reinforcement learning robotics control”

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1. 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 · 704 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

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

   Zhongyu Li, Xuxin Cheng, X. Peng, et al. · 2021 · 2021 IEEE International Conference on Robotics and Automation (ICRA) · 306 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

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

   Zhongyu Li, X. Peng, Pieter Abbeel, et al. · 2024 · The International Journal of Robotics Research · 251 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

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

   Rongrong Liu, F. Nageotte, P. Zanne, et al. · 2021 · Robotics · 202 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

5. Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Tasks

   Xiang Zhang, Liting Sun, Zhian Kuang, et al. · 2021 · IEEE Robotics and Automation Letters · 125 cites

   Many manipulation tasks require robots to interact with unknown environments. In such applications, the ability to adapt the impedance according to different task phases and environment constraints is crucial for safety and performance. Although many approaches based on deep reinforcement learning (RL) and learning from demonstration (LfD) have been proposed to obtain variable impedance skills on contact-rich manipulation tasks, these skills are typically task-specific and could be sensitive to changes in task settings. This letter proposes an inverse reinforcement learning (IRL) based approach to recover both the variable impedance policy and reward function from expert demonstrations. We e

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

   Yanjiang Guo, Jianke Zhang, Xiaoyu Chen, et al. · 2025 · 2025 IEEE International Conference on Robotics and Automation (ICRA) · 123 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

7. Closed-loop Dynamic Control of a Soft Manipulator using Deep Reinforcement Learning

   Andrea Centurelli, Luca Arleo, Alessandro Rizzo, et al. · 2022 · IEEE Robotics and Automation Letters · 85 cites

   The focus of the research community in the soft robotic field has been on developing innovative materials, but the design of control strategies applicable to these robotic platforms is still an open challenge. This is due to their highly nonlinear dynamics which are difficult to model and the degree of stochasticity they often incorporate. Data-driven controllers based on neural networks have recently been explored as a viable solution to be employed for these manipulators. This paper presents a neural network-based closed-loop controller, trained by a deep reinforcement learning algorithm called Trust Region Policy Optimization (TRPO). The training takes place in simulation, using an approx

8. Robust and Versatile Bipedal Jumping Control through Reinforcement Learning

   Zhongyu Li, X. Peng, P. Abbeel, et al. · 2023 · Robotics: Science and Systems XIX · 84 cites

   This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world. We present a reinforcement learning framework for training a robot to accomplish a large variety of jumping tasks, such as jumping to different locations and directions. To improve performance on these challenging tasks, we develop a new policy structure that encodes the robot's long-term input/output (I/O) history while also providing direct access to a short-term I/O history. In order to train a versatile jumping policy, we utilize a multi-stage training scheme that includes different training stages for different obj