{"total":91,"rows":[{"id":"2307.15818","title":"RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control","created":"2023-07-28","cited_by_count":3080,"area":"mllm"},{"id":"2203.12601","title":"R3M: A Universal Visual Representation for Robot Manipulation","created":"2022-03-23","cited_by_count":694,"area":null},{"id":"2210.00030","title":"VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training","created":"2022-09-30","cited_by_count":395,"area":"offlinerl"},{"id":"2203.06173","title":"Masked Visual Pre-training for Motor Control","created":"2022-03-11","cited_by_count":248,"area":null},{"id":"2304.08488","title":"Affordances from Human Videos as a Versatile Representation for Robotics","created":"2023-04-17","cited_by_count":226,"area":null},{"id":"2306.00958","title":"LIV: Language-Image Representations and Rewards for Robotic Control","created":"2023-06-01","cited_by_count":188,"area":null},{"id":"2309.01918","title":"RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking","created":"2023-09-05","cited_by_count":169,"area":null},{"id":"2302.12766","title":"Language-Driven Representation Learning for Robotics","created":"2023-02-24","cited_by_count":165,"area":null},{"id":"2303.00905","title":"Open-World Object Manipulation using Pre-trained Vision-Language Models","created":"2023-03-02","cited_by_count":156,"area":"mllm"},{"id":"2203.03580","title":"The Unsurprising Effectiveness of Pre-Trained Vision Models for Control","created":"2022-03-07","cited_by_count":155,"area":null},{"id":"2502.05485","title":"HAMSTER: Hierarchical Action Models For Open-World Robot Manipulation","created":"2025-02-08","cited_by_count":130,"area":"vla"},{"id":"2302.00935","title":"Policy Expansion for Bridging Offline-to-Online Reinforcement Learning","created":"2023-02-02","cited_by_count":104,"area":"offlinerl"},{"id":"2402.15567","title":"Foundation Policies with Hilbert Representations","created":"2024-02-23","cited_by_count":94,"area":"deeprl"},{"id":"2111.03788","title":"d3rlpy: An Offline Deep Reinforcement Learning Library","created":"2021-11-06","cited_by_count":84,"area":"offlinerl"},{"id":"2307.03659","title":"Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation","created":"2023-07-07","cited_by_count":82,"area":"offlinerl"},{"id":"2204.11134","title":"Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?","created":"2022-04-23","cited_by_count":70,"area":null},{"id":"2311.02198","title":"Imitation Bootstrapped Reinforcement Learning","created":"2023-11-03","cited_by_count":64,"area":"offlinerl"},{"id":"2212.05698","title":"MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations","created":"2022-12-12","cited_by_count":62,"area":null},{"id":"2206.10369","title":"The State of Sparse Training in Deep Reinforcement Learning","created":"2022-06-17","cited_by_count":61,"area":null},{"id":"2402.05741","title":"Real-World Robot Applications of Foundation Models: A Review","created":"2024-02-08","cited_by_count":61,"area":"mllm"},{"id":"2407.20179","title":"Theia: Distilling Diverse Vision Foundation Models for Robot Learning","created":"2024-07-29","cited_by_count":61,"area":null},{"id":"2212.08860","title":"Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning","created":"2022-12-17","cited_by_count":60,"area":null},{"id":"2212.05749","title":"On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline","created":"2022-12-12","cited_by_count":56,"area":null},{"id":"2202.10324","title":"VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning","created":"2022-02-17","cited_by_count":52,"area":null},{"id":"2210.06849","title":"Retrospectives on the Embodied AI Workshop","created":"2022-10-13","cited_by_count":46,"area":null},{"id":"2305.18499","title":"Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning","created":"2023-05-29","cited_by_count":45,"area":"wm"},{"id":"2301.09816","title":"SMART: Self-supervised Multi-task pretrAining with contRol Transformers","created":"2023-01-24","cited_by_count":44,"area":null},{"id":"2402.13181","title":"DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models","created":"2024-02-20","cited_by_count":44,"area":"offlinerl"},{"id":"2407.18911","title":"HRP: Human Affordances for Robotic Pre-Training","created":"2024-07-26","cited_by_count":44,"area":null},{"id":"2307.01849","title":"Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning","created":"2023-07-04","cited_by_count":40,"area":"diffusion"},{"id":"2303.11330","title":"Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion","created":"2023-03-20","cited_by_count":39,"area":null},{"id":"2204.02393","title":"Learning to Drive by Watching YouTube Videos: Action-Conditioned Contrastive Policy Pretraining","created":"2022-04-05","cited_by_count":37,"area":null},{"id":"2210.07442","title":"Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds","created":"2022-10-14","cited_by_count":32,"area":null},{"id":"2304.06600","title":"Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation","created":"2023-04-13","cited_by_count":29,"area":null},{"id":"2307.03719","title":"Polybot: Training One Policy Across Robots While Embracing Variability","created":"2023-07-07","cited_by_count":28,"area":null},{"id":"2309.14236","title":"MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation","created":"2023-09-25","cited_by_count":28,"area":"wm"},{"id":"2310.07418","title":"Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages","created":"2023-10-11","cited_by_count":28,"area":null},{"id":"2402.14795","title":"CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation","created":"2024-02-22","cited_by_count":28,"area":"offlinerl"},{"id":"2301.01006","title":"Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling","created":"2023-01-03","cited_by_count":26,"area":"driving"},{"id":"2404.01491","title":"SUGAR: Pre-training 3D Visual Representations for Robotics","created":"2024-04-01","cited_by_count":26,"area":null},{"id":"2210.04561","title":"A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning","created":"2022-10-10","cited_by_count":25,"area":null},{"id":"2206.05266","title":"Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?","created":"2022-06-10","cited_by_count":24,"area":null},{"id":"2307.03567","title":"SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks","created":"2023-07-07","cited_by_count":24,"area":null},{"id":"2404.17521","title":"Ag2Manip: Learning Novel Manipulation Skills with Agent-Agnostic Visual and Action Representations","created":"2024-04-26","cited_by_count":24,"area":null},{"id":"2302.09772","title":"Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot","created":"2023-02-20","cited_by_count":23,"area":null},{"id":"2306.03346","title":"Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data","created":"2023-06-06","cited_by_count":23,"area":null},{"id":"2506.09284","title":"UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation","created":"2025-06-10","cited_by_count":23,"area":null},{"id":"2304.04591","title":"For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal","created":"2023-04-10","cited_by_count":22,"area":"offlinerl"},{"id":"2109.01097","title":"The Functional Correspondence Problem","created":"2021-09-02","cited_by_count":20,"area":null},{"id":"2306.02865","title":"Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Critic","created":"2023-06-05","cited_by_count":20,"area":null},{"id":"2402.18137","title":"DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning","created":"2024-02-28","cited_by_count":20,"area":null},{"id":"2310.02635","title":"Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own","created":"2023-10-04","cited_by_count":19,"area":"agents"},{"id":"2211.03959","title":"Pretraining in Deep Reinforcement Learning: A Survey","created":"2022-11-08","cited_by_count":18,"area":null},{"id":"2311.18044","title":"Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges","created":"2023-11-29","cited_by_count":18,"area":null},{"id":"2409.00951","title":"Semantically Controllable Augmentations for Generalizable Robot Learning","created":"2024-09-02","cited_by_count":18,"area":null},{"id":"2306.10474","title":"A Universal Semantic-Geometric Representation for Robotic Manipulation","created":"2023-06-18","cited_by_count":16,"area":null},{"id":"2307.00972","title":"MoVie: Visual Model-Based Policy Adaptation for View Generalization","created":"2023-07-03","cited_by_count":16,"area":null},{"id":"2401.02814","title":"Object-Centric Instruction Augmentation for Robotic Manipulation","created":"2024-01-05","cited_by_count":16,"area":null},{"id":"2210.10763","title":"On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning","created":"2022-10-19","cited_by_count":15,"area":null},{"id":"2502.05454","title":"Temporal Representation Alignment: Successor Features Enable Emergent Compositionality in Robot Instruction Following","created":"2025-02-08","cited_by_count":14,"area":null},{"id":"2505.24061","title":"Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning","created":"2025-05-29","cited_by_count":14,"area":"deeprl"},{"id":"2406.10615","title":"Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation","created":"2024-06-15","cited_by_count":13,"area":null},{"id":"2312.17116","title":"Generalizable Visual Reinforcement Learning with Segment Anything Model","created":"2023-12-28","cited_by_count":12,"area":null},{"id":"2408.14785","title":"Unsupervised-to-Online Reinforcement Learning","created":"2024-08-27","cited_by_count":11,"area":"offlinerl"},{"id":"2310.03023","title":"Human-oriented Representation Learning for Robotic Manipulation","created":"2023-10-04","cited_by_count":10,"area":null},{"id":"2310.00771","title":"Pre-training with Synthetic Data Helps Offline Reinforcement Learning","created":"2023-10-01","cited_by_count":9,"area":"offlinerl"},{"id":"2305.16379","title":"Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning","created":"2023-05-25","cited_by_count":8,"area":null},{"id":"2401.13362","title":"TraKDis: A Transformer-based Knowledge Distillation Approach for Visual Reinforcement Learning with Application to Cloth Manipulation","created":"2024-01-24","cited_by_count":8,"area":"deeprl"},{"id":"2403.10760","title":"CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects","created":"2024-03-16","cited_by_count":8,"area":null},{"id":"2508.17230","title":"4D Visual Pre-training for Robot Learning","created":"2025-08-24","cited_by_count":8,"area":"diffusion"},{"id":"2406.03102","title":"DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays","created":"2024-06-05","cited_by_count":7,"area":"deeprl"},{"id":"2412.13662","title":"When Should We Prefer State-to-Visual DAgger Over Visual Reinforcement Learning?","created":"2024-12-18","cited_by_count":7,"area":"deeprl"},{"id":"2212.14538","title":"Transformer in Transformer as Backbone for Deep Reinforcement Learning","created":"2022-12-30","cited_by_count":6,"area":null},{"id":"2307.10936","title":"PASTA: Pretrained Action-State Transformer Agents","created":"2023-07-20","cited_by_count":6,"area":"agents"},{"id":"2306.07552","title":"Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-Second","created":"2023-06-13","cited_by_count":5,"area":null},{"id":"2312.15863","title":"PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning","created":"2023-12-26","cited_by_count":5,"area":null},{"id":"2502.04773","title":"An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks","created":"2025-02-07","cited_by_count":5,"area":null},{"id":"2602.04315","title":"GeneralVLA: Generalizable Vision-Language-Action Models with Knowledge-Guided Trajectory Planning","created":"2026-02-04","cited_by_count":5,"area":"vla"},{"id":"2206.08883","title":"CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer","created":"2022-06-17","cited_by_count":4,"area":null},{"id":"2211.15992","title":"MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents","created":"2022-11-29","cited_by_count":3,"area":null},{"id":"2404.18896","title":"Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models","created":"2024-04-29","cited_by_count":2,"area":"wm"},{"id":"2502.05555","title":"Efficient Reinforcement Learning Through Adaptively Pretrained Visual Encoder","created":"2025-02-08","cited_by_count":2,"area":"deeprl"},{"id":"2507.07197","title":"Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning","created":"2025-07-09","cited_by_count":2,"area":"deeprl"},{"id":"2211.08234","title":"Build generally reusable agent-environment interaction models","created":"2022-11-13","cited_by_count":1,"area":null},{"id":"2303.13489","title":"Boosting Reinforcement Learning and Planning with Demonstrations: A Survey","created":"2023-03-23","cited_by_count":1,"area":null},{"id":"2310.01404","title":"H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2406.03731","title":"Quality-Diversity with Limited Resources","created":"2024-06-06","cited_by_count":1,"area":null},{"id":"2502.19544","title":"Efficient Reinforcement Learning by Guiding World Models with Non-Curated Data","created":"2025-02-26","cited_by_count":1,"area":"wm"},{"id":"2312.12339","title":"Value Explicit Pretraining for Learning Transferable Representations","created":"2023-12-19","cited_by_count":0,"area":null},{"id":"2406.06072","title":"Adapting Pretrained ViTs with Convolution Injector for Visuo-Motor Control","created":"2024-06-10","cited_by_count":0,"area":null},{"id":"2503.02405","title":"A comparison of visual representations for real-world reinforcement learning in the context of vacuum gripping","created":"2025-03-04","cited_by_count":0,"area":"deeprl"}],"next_cursor":null}