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Research papers on Reinforcement learning in robotics

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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 · 778 citations

    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 · 475 citations

    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 · 310 citations

    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 · 266 citations

    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 citations

    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 · 180 citations

    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) · 140 citations

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

    Xiang Zhang, Li-Ting Sun, Zhian Kuang, et al. · 2021 · IEEE Robotics and Automation Letters · 127 citations

    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

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  10. 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 · 87 citations

    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

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  11. Robust and Versatile Bipedal Jumping Control through Reinforcement Learning

    Zhongyu Li, Xue-Bin Peng, P. Abbeel, et al. · 2023 · Robotics: Science and Systems XIX · 84 citations

    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

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  13. RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning

    Kun Lei, Huan-Yu Li, Dongjie Yu, et al. · 2025 · Science robotics · 76 citations

    Real-world robotic manipulation in homes and factories demands reliability, efficiency, and robustness that approach or surpass skilled human operators. We present a real-world reinforcement learning (RL) framework, RL-100, for achieving complete task success under a predefined evaluation protocol built on diffusion visuomotor policies. RL-100 unifies imitation and RL under a single clipped proximal policy optimization surrogate objective applied in the denoising process, yielding conservative, stable improvements across offline and online stages. To meet deployment latency, a lightweight consistency distillation compresses multistep diffusion into a one-step controller for high-frequency co

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  14. Apprenticeship learning and reinforcement learning with application to robotic control

    Pieter Abbeel · 2008 · ProQuest Demo Repository · 71 citations

    Many problems in robotics have unknown, stochastic, high-dimensional, and highly nonlinear dynamics, and offer significant challenges to both traditional control methods and reinforcement learning algorithms. Some of the key difficulties that arise in these problems are: (i) It is often difficult to write down, in closed form, a formal specification of the control task. For example, what is the objective function for "flying well"? (ii) It is often difficult to build a good dynamics model because of both data collection and data modeling challenges (similar to the "exploration problem" in reinforcement learning). (iii) It is often computationally expensive to find closed-loop controllers for

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  15. Adaptive Control and Intersections with Reinforcement Learning

    A. Annaswamy · 2023 · Annu. Rev. Control. Robotics Auton. Syst. · 64 citations

    This article provides an exposition of the field of adaptive control and its intersections with reinforcement learning. Adaptive control and reinforcement learning are two different methods that are both commonly employed for the control of uncertain systems. Historically, adaptive control has excelled at real-time control of systems with specific model structures through adaptive rules that learn the underlying parameters while providing strict guarantees on stability, asymptotic performance, and learning. Reinforcement learning methods are applicable to a broad class of systems and are able to produce near-optimal policies for highly complex control tasks. This is often enabled by signific

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  16. Reinforcement Learning and Control of a Lower Extremity Exoskeleton for Squat Assistance

    Shu-Zhen Luo, Ghaith J. Androwis, Sergei V. Adamovich, et al. · 2021 · Frontiers in Robotics and AI · 58 citations

    A significant challenge for the control of a robotic lower extremity rehabilitation exoskeleton is to ensure stability and robustness during programmed tasks or motions, which is crucial for the safety of the mobility-impaired user. Due to various levels of the user’s disability, the human-exoskeleton interaction forces and external perturbations are unpredictable and could vary substantially and cause conventional motion controllers to behave unreliably or the robot to fall down. In this work, we propose a new, reinforcement learning-based, motion controller for a lower extremity rehabilitation exoskeleton, aiming to perform collaborative squatting exercises with efficiency, stability, and

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  20. Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments From Temporal Logic Specifications

    Mingyu Cai, Erfan Aasi, C. Belta, et al. · 2022 · IEEE Robotics and Automation Letters · 37 citations

    Model-free continuous control for robot navigation tasks using Deep Reinforcement Learning (DRL) that relies on noisy policies for exploration is sensitive to the density of rewards. In practice, robots are usually deployed in cluttered environments, containing many obstacles and narrow passageways. Designing dense effective rewards is challenging, resulting in exploration issues during training. Such a problem becomes even more serious when tasks are described using temporal logic specifications. This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) fo

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  21. Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions

    Desong Du, Shao-Fu Han, N. Qi, et al. · 2023 · 2023 IEEE International Conference on Robotics and Automation (ICRA) · 35 citations

    Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the absence of strong safety guarantees. To overcome this challenge, this paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data without explicitly employing a dynamic model. We also proposed the Lyapunov barrier actor-critic (LBAC), a model-free RL algorithm, to search for a controller that satisfies the data-based approximation of the safety and reachability conditions. The proposed approach is demonstrated through simulation and real-world robot control

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  22. Hybrid control for combining model-based and model-free reinforcement learning

    Allison Pinosky, Ian Abraham, Alexander Broad, et al. · 2022 · The International Journal of Robotics Research · 34 citations

    We develop an approach to improve the learning capabilities of robotic systems by combining learned predictive models with experience-based state-action policy mappings. Predictive models provide an understanding of the task and the dynamics, while experience-based (model-free) policy mappings encode favorable actions that override planned actions. We refer to our approach of systematically combining model-based and model-free learning methods as hybrid learning. Our approach efficiently learns motor skills and improves the performance of predictive models and experience-based policies. Moreover, our approach enables policies (both model-based and model-free) to be updated using any off-poli

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  23. CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions

    Li-Zhi Yang, Blake Werner, Massimiliano de Sa, et al. · 2025 · 2026 IEEE International Conference on Robotics and Automation (ICRA) · 33 citations

    Reinforcement learning (RL), while powerful and expressive, can often prioritize performance at the expense of safety. Yet safety violations can lead to catastrophic outcomes in real-world deployments. Control Barrier Functions (CBFs) offer a principled method to enforce dynamic safety—traditionally deployed online via safety filters. While the result is safe behavior, the fact that the RL policy does not have knowledge of the CBF can lead to conservative behaviors. This paper proposes CBF-RL, a framework for generating safe behaviors with RL by enforcing CBFs in training. CBF-RL has two key attributes: (1) minimally modifying a nominal RL policy to encode safety constraints via a CBF term,

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  24. Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots

    Yu-Feng Yuan, A. Mahmood · 2022 · 2022 International Conference on Robotics and Automation (ICRA) · 33 citations

    An oft-ignored challenge of real-world reinforcement learning is that the real world does not pause when agents make learning updates. As standard simulated environments do not address this real-time aspect of learning, most available implementations of RL algorithms process environment interactions and learning updates sequentially. As a consequence, when such implementations are deployed in the real world, they may make decisions based on significantly delayed observations and not act responsively. Asynchronous learning has been proposed to solve this issue, but no systematic comparison between sequential and asynchronous reinforcement learning was conducted using real-world environments.

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