RL for Robotics

RL: Reinforcement Learning

Reinforcement learning for robotics studies how robots can learn actions that achieve goals while coping with contact, uncertainty, and changing environments. Common methods include PPO, SAC, and TD3, often trained in parallel simulation with domain randomization; imitation learning, motion priors, and pretrained vision-language-action models increasingly provide useful starting points. Work spans humanoid locomotion, aerial and mobile navigation, and manipulation that uses tactile feedback, with safety filters and learned world models helping address risky or partially observed tasks. A central challenge is making learning more sample-efficient while ensuring that simulated skills and learned rewards remain reliable on real robots and unfamiliar situations.

Latest papers

253 papers45 papers in the last four weeks, up 45% on the four weeks before. 0.3% of all new papers.

Jan 30, 2026

PlatoLTL: Scaling LTL-Guided Multi-Task RL

Linear temporal logic (LTL) has emerged as a powerful formalism for specifying structured, temporally extended tasks in multi-task reinforcement learning (RL). However, while existing approaches in LTL-guided multi-task RL demonstrate success in simple environments, they suffer from challenges in representation learning and exploration efficiency when applied to high-dimensional environments with parameterized specifications. We present PlatoLTL, which elevates state-of-the-art methods to address both challenges. We model atomic propositions as instances of atomic predicates and inject task parameters directly into the goal embedding to enable efficient generalization. We also leverage simple priors on closeness to predicate satisfaction to enable and accelerate learning of complex tasks without biasing the optimal policy. We validate our approach on challenging environments including robotic manipulation and multi-drone navigation.

Jacques Cloete, Mathias Jackermeier, Ioannis Havoutis +1

Oxford Robotics Institute University of Oxford Oxford, United Kingdom · Department of Computer Science University of Oxford Oxford, United Kingdom

RL for Robotics Constrained RL Multi-Task RL Generalization in RL Goal-Conditioned RL

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Jan 28, 2026

OGPO: One-Step Generative Policy Optimization for Real-Time Robot Control

Real-time robot control demands fast action generation. Diffusion and flow matching policies for robot control require multi-step sampling, limiting their deployment in real-time scenarios. Natively reducing the sampling steps to one sacrifices representation quality and task performance, creating a trilemma among speed, fidelity, and performance. We present One-Step Generative Policy Optimization (OGPO), a systematic framework to resolve this trilemma. OGPO first pairs a lightweight architecture with the interval velocity principle for distillation-free one-step inference, while representation spreading prevents representation quality degradation. It then performs on-policy reinforcement learning (RL) fine-tuning on this fast, stable policy to break the imitation learning ceiling. Experiments on RoboMimic and OpenAI Gym benchmarks show that OGPO matches or exceeds multi-step baselines while achieving a 5-20 times inference speedup and over 120Hz control frequency. Physical deployment on a Franka-Emika-Panda robot validates real-world applicability. Project page: https://ogpo-project.github.io/

Guowei Zou, Haitao Wang, Hejun Wu +3

School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China

Robotic Control Real-Time Robot Control RL for Robotics Robot Policy Learning Robot Imitation Learning

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Jan 27, 2026

Reversible Simplex Supervision with Post-Action Debt Accounting for Goal-Reaching RL

Deploying reinforcement learning (RL) on multi-tonne robots calls for supervisory mechanisms that address both operational safety and progress toward task completion. However, repeated switching need not preserve task progress when a learned action increases storage before recovery takes control. We introduce reversible Simplex supervision with post-action debt accounting for a frozen finite-state policy and robust-adaptive recovery. Under exact sampled-state information and stated model and certificate conditions, we prove that recovery repayment exceeding a uniform triggering-edge debt bound guarantees finite switching and finite-sample goal entry. We formulate reachability-based certificate constructions for establishing these sufficient conditions. In 20 matched simulations, goal-entry counts are 20 with debt gating and 18 without it; the two remaining runs terminate under the supervisor's admissibility stopping rule. On an experimental 6000 kg robot, 24 asphalt and soft-terrain trials evaluate 50 ms supervision above a 1 kHz actuator stack; all eight triggered recoveries complete debt-gated re-entry. The experiments demonstrate the supervisory mechanism in the tested trials.

Mehdi Heydari Shahna, Joongheon Kim, Jouni Mattila

Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland · Department of Electrical and Computer Engineering, Korea University, Seoul, Republic of Korea

RL for Robotics Robot Safety Runtime Assurance Safe RL

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Jan 22, 2026

AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning

Object-Goal Navigation (ObjectNav) requires an agent to autonomously explore an unknown environment and navigate toward target objects specified by a semantic label. While prior work has primarily studied zero-shot ObjectNav under 2D locomotion, extending it to aerial platforms with 3D locomotion capability remains underexplored. Aerial robots offer superior maneuverability and search efficiency, but also introduce new challenges in spatial perception, dynamic control, and safety assurance. In this paper, we propose AION for vision-based aerial ObjectNav without relying on external localization or global maps. AION is an end-to-end dual-policy reinforcement learning (RL) framework that decouples exploration and goal-reaching behaviors into two specialized policies. We evaluate AION on the AI2-THOR benchmark and further assess its real-time performance in IsaacSim using high-fidelity drone models. Experimental results show that AION achieves superior performance across comprehensive evaluation metrics in exploration, navigation efficiency, and safety. The project is available at https://github.com/Zichen-Yan/AION.

Zichen Yan, Yuchen Hou, Shenao Wang +3

Department of Electrical and Computer Engineering, National University of Singapore, Singapore.

RL for Robotics Aerial Robotics Vision-Based Robot Control Object-Goal Navigation

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Jan 12, 2026

SHARE: Sharing Human Actions across Robot Embodiments through Unified Retargeting and Tracking

The central challenge in reusing human actions across robots is the need to realize shared intent through incompatible kinematics, dynamics, and control interfaces. We present SHARE, which decomposes action sharing into two connected stages---reference generation and feedback execution---and uses native robot references as their interface to achieve shared structure, native realization. In the reference-generation stage, AdaMorph extracts human-motion intent with a shared temporal backbone and generates embodiment-compatible references through robot-conditioned decoding. In the feedback-execution stage, AdaTrack combines these references with proprioception, action history, and morphology, then uses variable-length dynamic representations, morphology-conditioned attention, expert distillation, and closed-loop reinforcement learning to emit native actions from one shared Actor. On G1, HI, AD, and T1, AdaMorph attains seen/unseen root-speed Pearson correlation coefficients (PCCs) of 0.951/0.894; AdaTrack reaches 94.00%/91.96% closed-loop Success Rate with 21.15% of the total parameters in four specialist Actors, while cross-embodiment pretraining improves seen/unseen adaptation by 9.67/13.62 points with only 1% target data.

Junzhe Xu, Haoyu Zhang, Yunheng Wang +6

JD Explore Academy Beijing, China

Human-to-Robot Motion Imitation Cross-Embodiment Robot Learning Motion Retargeting Humanoid Robot Control Robot Imitation Learning RL for Robotics

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Nov 14, 2025

Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum

Humanoid robots have demonstrated strong capabilities for interacting with static scenes across locomotion and manipulation, yet dynamic real-world interactions remain challenging. As a step toward fast-moving object interactions, we present an RL training pipeline that yields a unified whole-body controller for humanoid badminton, coordinating footwork and striking without motion priors or expert demonstrations. In badminton, locomotion and striking are tightly coupled, making the final hitting objective difficult to optimize directly due to sparse rewards and conflicting gradients. We address this with an annealed curriculum that first stabilizes learning through auxiliary locomotion objectives, then progressively removes them to focus optimization on the final hitting objective. For deployment, we use an Extended Kalman Filter (EKF) to estimate and predict shuttlecock trajectories for target striking, and also develop a prediction-free variant that removes the EKF and explicit prediction. We validate the framework in simulation and on hardware. In simulation, two robots sustain a rally of 21 consecutive hits. In real-world tests with both machine-fed shuttles and human-robot rallies, the robot achieves outgoing shuttle speeds up to 19.1 m/s. Moreover, the prediction-free variant attains comparable performance to the EKF-based policy. Overall, our approach enables dynamic yet precise goal striking in humanoid badminton and suggests a path toward more dynamics-critical whole-body interaction tasks.

Chenhao Liu, Leyun Jiang, Ningyuan Tian +4

RL for Robotics Reinforcement Learning Humanoid Robot Control Curriculum RL Robotics

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Oct 30, 2025

Thor: Towards Human-Inspired Whole-Body Reactions for Intense Contact-Rich Environments

Maintaining whole-body stability and motion tracking under large interaction forces remains challenging for humanoids. We present Thor, a reinforcement learning framework for forceful humanoid loco-manipulation. Thor jointly trains lower-body, waist, and upper-body policies with shared whole-body observations and body-specific rewards to coordinate locomotion and force adaptation, waist posture regulation, and upper-body motion tracking. We further introduce a force-adaptive torso-tilt (FAT2) objective that derives a load-dependent horizontal center-of-mass offset reference from quasi-static moment balance. Capacity-matched simulation blations show that the three-policy architecture improves tracking under large external force disturbances, while real-world ablations demonstrate that FAT2 increases peak pulling capability. On the Unitree G1, Thor achieves mean peak dual-hand pulling forces of 167.7 N and 145.5 N during backward and forward locomotion, exceeding the best-performing baseline by 68.9% and 74.7%, respectively. Real-world demonstrations include opening a fire door with one hand using approximately 60 N of pulling force and towing a 1.7-ton passenger car.

Gangyang Li, Hongzhe Shi, Qing Shi +5

Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology · Beijing Academy of Artificial Intelligence

RL for Robotics Humanoid Loco-Manipulation Contact-Rich Robotic Manipulation Humanoid Robot Control Whole-Body Control

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Oct 16, 2025

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

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, (2) and safety filtering of the policy rollouts in training. Theoretically, we prove that continuous-time safety filters can be deployed via closed-form expressions on discrete-time roll-outs. Practically, we demonstrate that CBF-RL internalizes the safety constraints in the learned policy -- both enforcing safer actions and biasing towards safer rewards -- enabling safe deployment without the need for an online safety filter. We validate our framework through ablation studies on navigation tasks and on the Unitree G1 humanoid robot, where CBF-RL enables safer exploration, faster convergence, and robust performance under uncertainty, enabling the humanoid robot to avoid obstacles and climb stairs safely in real-world settings without a runtime safety filter.

Lizhi Yang, Blake Werner, Massimiliano de Sa +1

Caltech MCE

RL for Robotics Control Barrier Functions Constrained RL Safety Filtering Safe RL

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Oct 14, 2025

Pretraining in Actor-Critic Reinforcement Learning for Locomotion

The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years. However, in the domain of reinforcement learning (RL) for robot locomotion, individual skills are often learned from scratch despite the high likelihood that some generalizable knowledge is shared across all task-specific policies belonging to the same robot embodiment. This work aims to define a paradigm for pretraining neural network models that encapsulate such knowledge and can subsequently serve as a basis for warm-starting the RL process in classic actor-critic algorithms, such as Proximal Policy Optimization (PPO). We begin with a task-agnostic exploration-based data collection algorithm to gather diverse, dynamic transition data, which is then used to train a Proprioceptive Inverse Dynamics Model (PIDM) through supervised learning. The pretrained weights are then loaded into both the actor and critic networks to warm-start the policy optimization of actual tasks. We systematically validated our proposed method with 9 distinct robot locomotion RL environments comprising 3 different robot embodiments, showing significant benefits of this initialization strategy. Our proposed approach on average improves sample efficiency by 36.2% and task performance by 4.3% compared to random initialization. We further present key ablation studies and empirical analyses that shed light on the mechanisms behind the effectiveness of this method.

Jiale Fan, Andrei Cramariuc, Tifanny Portela +1

Section of Microengineering, EPFL, 1015 Lausanne, Switzerland · Robotic Systems Lab, ETH Zurich, 8092 Zürich, Switzerland · ETH AI Center, ETH Zurich, 8092 Zürich, Switzerland

RL for Robotics Reinforcement Learning Robot Locomotion Actor-Critic Methods

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Aug 26, 2025

Learning Acrobatic Flight from Preferences

Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where objectives are difficult to formalize or inherently subjective. Acrobatic flight poses a particularly challenging problem due to its complex dynamics, rapid movements, and the importance of precise execution. However, manually designed reward functions for such tasks often fail to capture the qualities that matter: we find that hand-crafted rewards agree with human judgment only 60.7% of the time, underscoring the need for preference-driven approaches. In this work, we propose Reward Ensemble under Confidence (REC), a probabilistic reward learning framework for PbRL that explicitly models per-timestep reward uncertainty through an ensemble of distributional reward models. By propagating uncertainty into the preference loss and leveraging disagreement for exploration, REC achieves 88.4% of shaped reward performance on acrobatic quadrotor control, compared to 55.2% with standard Preference PPO. We train policies in simulation and successfully transfer them zero-shot to the real world, demonstrating complex acrobatic maneuvers learned purely from preference feedback. We further validate REC on a continuous control benchmark, confirming its applicability beyond the domain of aerial robotics.

Colin Merk, Ismail Geles, Jiaxu Xing +3

Robotics and Perception Group, University of Zurich, Switzerland · Autonomous Learning and Predictive Intelligence Lab, University of Zurich, Switzerland

RL for Robotics Aerial Robotics Reinforcement Learning Preference-Based RL

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Feb 25, 2025

RetrDex: Efficient Object Retrieval in Cluttered Scenes with a Dexterous Hand

Retrieving objects buried beneath clutter is both challenging and time-consuming, as complex support relationships make manipulation particularly difficult. Existing methods either focus on support relations and rely on sequential grasping to remove occluding objects, or perform preparatory actions such as pushing to facilitate subsequent grasps. However, these approaches are often inefficient and treat physical interactions as isolated auxiliary steps. In this paper, we propose RetrDex, an efficient framework for dexterous arm-hand systems to learn object retrieval in cluttered scenes. Our approach leverages large-scale parallel reinforcement learning (RL) in diverse cluttered scenes and incorporates a spatially aware representation that encodes occlusion patterns and spatial relationships among the target, the dexterous hand, and surrounding clutter. This representation enables the policy to develop diverse manipulation skills (e.g., pushing, stirring, and poking) that actively clear occluders. We evaluate RetrDex on 16 household objects across varied clutter configurations, and obtain strong retrieval performance and efficiency on both seen and unseen targets. Furthermore, we demonstrate successful zero-shot transfer to a real-world dexterous multi-fingered robot system, validating the practical applicability of our method. Videos can be found on our project website: https://RetrDex.github.io.

Fengshuo Bai, Yu Li, Jie Chu +5

Shanghai Jiao Tong University · PKU-PsiBot Joint Lab · Peking University +2

RL for Robotics Robotic Manipulation

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Aug 26, 2024

A Survey on Reinforcement Learning Applications in SLAM

Simultaneous localization and mapping (SLAM) allows a mobile robot or autonomous vehicle to build a map of an unknown environment while estimating its own pose within that map. Reinforcement learning (RL), in which an agent learns a decision policy from interaction and reward, has been applied to decide how such systems move, explore, and recognize places they have visited before. This survey reviews the applications of RL in SLAM. We first distinguish passive SLAM, in which the robot's motion is not chosen by the SLAM system, from active SLAM, in which it is, and summarize the sensors that provide the input to SLAM. We then introduce the RL methods used in this literature, from value-based and policy-based methods to actor-critic and deep RL. Next, we classify RL applications in SLAM into three categories: path planning, including environment exploration and obstacle avoidance; loop closure detection; and active SLAM. Thirteen representative studies are compared in terms of their simulation environment, deep learning method, SLAM method, and RL algorithm. Most of these studies are evaluated mainly in simulation, and value-based methods from the deep Q-network family are the most common. Finally, we discuss the challenges of applying RL to SLAM, namely computational demands, safety, generalization, high-dimensional state and action spaces, sample efficiency, and sensor and actuator delays, and we outline directions for future research.

Mohammad Dehghani Tezerjani, Mohammad Khoshnazar, Mohammadhamed Tangestanizadeh +2

Computer Science and Engineering, University of North Texas, Denton, USA · Institute of Artificial Intelligence, University of Bremen, Bremen, Germany · Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, USA +1

Simultaneous Localization and Mapping RL for Robotics Robotic RL Loop Closure Detection

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Jul 29, 2023

Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip

Reinforcement learning (RL) performs well in continuous-control tasks, but evolving hidden disturbances create partial observability: the agent must infer decision-relevant latent dynamics from interaction history. This study investigates how observation history, action history, history length, and network structure affect recurrent Twin Delayed Deep Deterministic Policy Gradient (TD3) agents. Three recurrent architectures are evaluated under controlled disturbances with different temporal characteristics. Results show that action history is particularly important when observed responses depend on previous actions, and that processing past and current action-observation information within a unified temporal sequence improves performance compared with using separate branches. We also introduce H-TD3, which reuses recurrent states generated by the actor to initialize the critic, reducing duplicated sequence processing. The architectures are further tested in a simulation-based differential-drive rover motion-regulation task under hidden asymmetric wheel slip. Recurrent architectures retain their advantage under the physically motivated multiplicative wheel-slip model, while policies trained with abstract temporally structured disturbances transfer more effectively to previously unseen wheel-slip dynamics than policies trained without disturbances. These findings provide practical guidance for recurrent RL under partial observability and evolving hidden disturbances.

Saki Omi, Hyo-Sang Shin, Namhoon Cho +2

Space Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg · School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, U.K. · Department of Aerospace Engineering, Seoul National University, Seoul, South Korea

RL for Robotics Reinforcement Learning Partially Observable RL Robust RL

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