Key papers

• Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones (2020)
  Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, et al.
  16.61
• Perpetual Humanoid Control For Real-time Simulated Avatars (2023)
  Zhengyi Luo, Jinkun Cao, Alexander Winkler, et al.
  13.84
• Meta Reinforcement Learning For Sim-to-real Domain Adaptation (2019)
  Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, et al.
  13.74
• Sim2real Transfer For Reinforcement Learning Without Dynamics Randomization (2020)
  Manuel Kaspar, Juan David Munoz Osorio, Jürgen Bock
  13.74
• Self-organizing Maps For Storage And Transfer Of Knowledge In Reinforcement Learning (2018)
  Thommen George Karimpanal, Roland Bouffanais
  13.50
• In-Context World Modeling for Robotic Control (2026)
  Siyin Wang et al.
  12.82
• Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-based RL For Sparse Cost Robotic Tasks (2019)
  Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, et al.
  12.81
• Accelerated Sim-to-real Deep Reinforcement Learning: Learning Collision Avoidance From Human Player (2021)
  Hanlin Niu, Ze Ji, Farshad Arvin, et al.
  11.49
• Visuomotor Mechanical Search: Learning To Retrieve Target Objects In Clutter (2020)
  Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni, et al.
  11.19
• GEM: Generative Supervision Helps Embodied Intelligence (2026)
  Ruowen Zhao et al.
  11.11
• Learning To Locomote: Understanding How Environment Design Matters For Deep Reinforcement Learning (2020)
  Daniele Reda, Tianxin Tao, Michiel van de Panne
  10.97
• GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning (2026)
  GigaBrain Team: Boyuan Wang et al.
  10.63
• Deep Reinforcement Learning With A Stage Incentive Mechanism Of Dense Reward For Robotic Trajectory Planning (2020)
  Gang Peng, Jin Yang, Xinde Lia, et al.
  10.61
• An Optimal Online Method Of Selecting Source Policies For Reinforcement Learning (2017)
  Siyuan Li, Chongjie Zhang
  9.76
• Masked Visual Actions for Unified World Modeling (2026)
  Hadi Alzayer et al.
  9.73
• Rl_reach: Reproducible Reinforcement Learning Experiments For Robotic Reaching Tasks (2021)
  Pierre Aumjaud, David McAuliffe, Francisco Javier Rodríguez Lera, et al.
  9.18
• Efficient Preference-based Reinforcement Learning Using Learned Dynamics Models (2023)
  Yi Liu, Gaurav Datta, Ellen Novoseller, et al.
  8.82
• PhysisForcing: Physics Reinforced World Simulator for Robotic Manipulation (2026)
  Peiwen Zhang et al.
  8.79
• GWM: Towards Scalable Gaussian World Models for Robotic Manipulation (2025)
  Guanxing Lu et al.
  8.71
• Learning From Imperfect Demonstrations From Agents With Varying Dynamics (2021)
  Zhangjie Cao, Dorsa Sadigh
  8.60
• Episodic Self-imitation Learning With Hindsight (2020)
  Tianhong Dai, Hengyan Liu, Anil Anthony Bharath
  8.09
• Curriculum Goal Masking For Continuous Deep Reinforcement Learning (2018)
  Manfred Eppe, Sven Magg, Stefan Wermter
  7.81
• Real-time Simulation Enabled Navigation Control Of Magnetic Soft Continuum Robots In Confined Lumens (2025)
  Dezhong Tong, Zhuonan Hao, Jiyu Li, et al.
  7.80
• SeededGrasp: Language-Guided Grasping in Complex Scenes with Multiple Embodiments (2026)
  Yang Xu et al.
  7.37
• Real-time Trajectory Planning For Automated Vehicle Safety And Performance In Dynamic Environments (2020)
  Huckleberry Febbo, Paramsothy Jayakumar, Jeffrey L. Stein, et al.
  7.16
• Reinforcement Learning Via Recurrent Convolutional Neural Networks (2017)
  Tanmay Shankar, Santosha K. Dwivedy, Prithwijit Guha
  7.16
• Learning To Transfer Dynamic Models Of Underactuated Soft Robotic Hands (2020)
  Liam Schramm, Avishai Sintov, Abdeslam Boularias
  6.77
• Rethinking Population-assisted Off-policy Reinforcement Learning (2023)
  Bowen Zheng, Ran Cheng
  6.77
• GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation (2025)
  Quanhao Qian et al.
  6.75
• Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots (2025)
  Mihir Kulkarni et al.
  6.63
• DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning (2025)
  Zhengrong Xue et al.
  6.12
• Vid2sim: Realistic And Interactive Simulation From Video For Urban Navigation (2025)
  Ziyang Xie, Zhizheng Liu, Zhenghao Peng, et al.
  6.07
• Demonstrating Berkeley Humanoid Lite: An Open-source, Accessible, and Customizable 3D-printed Humanoid Robot (2025)
  Yufeng Chi et al.
  5.96
• Learning Multi-Stage Pick-and-Place with a Legged Mobile Manipulator (2025)
  Haichao Zhang et al.
  5.93
• ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning (2025)
  Kailin Li et al.
  5.90
• RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies (2026)
  Tianxing Chen et al.
  5.88
• A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards (2025)
  Shivansh Patel et al.
  5.84
• Autonomous Assessment Of Demonstration Sufficiency Via Bayesian Inverse Reinforcement Learning (2022)
  Tu Trinh, Haoyu Chen, Daniel S. Brown
  5.84
• Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack (2026)
  He Zhang et al.
  5.82
• Underwater Robotic Simulators Review for Autonomous System Development (2025)
  Sara Aldhaheri et al.
  5.65
• OceanSim: A GPU-Accelerated Underwater Robot Perception Simulation Framework (2025)
  Jingyu Song et al.
  5.59
• Mobile Robot Navigation Using Hand-Drawn Maps: A Vision Language Model Approach (2025)
  Aaron Hao Tan et al.
  5.54
• Depth Transfer: Learning to See Like a Simulator for Real-World Drone Navigation (2025)
  Hang Yu et al.
  5.35
• Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic (2025)
  Lennart R\"ostel et al.
  5.35
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots (2025)
  NVIDIA: Johan Bjorck et al.
  5.24
• Deep Visual Servoing of an Aerial Robot Using Keypoint Feature Extraction (2025)
  Shayan Sepahvand et al.
  5.24
• GLIB: Efficient Exploration For Relational Model-based Reinforcement Learning Via Goal-literal Babbling (2020)
  Rohan Chitnis, Tom Silver, Joshua Tenenbaum, et al.
  5.24
• Action-quantized Offline Reinforcement Learning For Robotic Skill Learning (2023)
  Jianlan Luo, Perry Dong, Jeffrey Wu, et al.
  5.24
• Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators (2025)
  Yongliang Wang et al.
  5.15
• SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training (2025)
  Mingdong Wu et al.
  5.04
• PhysMani: Physics-principled 3D World Model for Dynamic Object Manipulation (2026)
  Peng Yun et al.
  5.01
• WorldSample: Closed-loop Real-robot RL with World Modelling (2026)
  Yuquan Xue et al.
  5.01
• Learning Diverse Humanoid Tasks via Synthetic Video Scenarios without Real World Data (2026)
  Yun-Hao Tsai et al.
  5.01
• Ordered Action Tokens for Visuomotor Policy Learning (2026)
  Chaoqi Liu et al.
  5.01
• ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation (2026)
  Yunao Huang et al.
  5.01
• Robot-Factored World Models via Robot Rendering (2026)
  Byungjun Kim et al.
  5.01
• Do as I Do: Dexterous Manipulation Data from Everyday Human Videos (2026)
  Bhawna Paliwal et al.
  4.95
• Learning Action Priors for Cross-embodiment Robot Manipulation (2026)
  Dong Jing et al.
  4.95
• Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries (2026)
  Jannik Drotleff et al.
  4.95
• NavIsaacLab: Generating Realistic Crowd via Parallel Robot Learning for Benchmarking Human-aware Navigation (2026)
  Bingyi Xia et al.
  4.95