mlsanityData to 7 Oct 2026

ALAN: Autonomously Exploring Robotic Agents in the Real World

arXiv 2302.06604 ↗First posted 13 Feb 2023cs.RO cs.AI cs.CVcited by 23

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Abstract (arXiv)

Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision. While it is possible to build agents that can learn in such a manner without supervision, current methods struggle to scale to the real world. Thus, we propose ALAN, an autonomously exploring robotic agent, that can perform tasks in the real world with little training and interaction time. This is enabled by measuring environment change, which reflects object movement and ignores changes in the robot position. We use this metric directly as an environment-centric signal, and also maximize the uncertainty of predicted environment change, which provides agent-centric exploration signal. We evaluate our approach on two different real-world play kitchen settings, enabling a robot to efficiently explore and discover manipulation skills, and perform tasks specified via goal images. Website at https://robo-explorer.github.io/

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 36 of its 47 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

36 of 47 references.

References · 36

  1. 2017Mask R-CNNcited by 7,259
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,598
  4. 2018World Modelscited by 1,444
  5. 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
  6. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,221
  7. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,173
  8. 2017Hindsight Experience Replaycited by 1,096
  9. 2020Mastering Atari with Discrete World Modelscited by 990
  10. 2015End-to-End Training of Deep Visuomotor Policiescited by 847
  11. 2019Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learningcited by 750
  12. 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 734
24 more
  1. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
  2. 2022R3M: A Universal Visual Representation for Robot Manipulationcited by 678
  3. 2020AWAC: Accelerating Online Reinforcement Learning with Offline Datasetscited by 647
  4. 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 635
  5. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 632
  6. 2022Detecting Twenty-thousand Classes using Image-level Supervisioncited by 613
  7. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 517
  8. 2020Planning to Explore via Self-Supervised World Modelscited by 365
  9. 2019Dynamics-Aware Unsupervised Discovery of Skillscited by 308
  10. 2016#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learningcited by 307
  11. 2019Self-Supervised Exploration via Disagreementcited by 263
  12. 2017Count-Based Exploration with Neural Density Modelscited by 255
  13. 2018Visual Reinforcement Learning with Imagined Goalscited by 248
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  18. 2021The Surprising Effectiveness of Representation Learning for Visual Imitationcited by 149
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  20. 2021Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videoscited by 116
  21. 2018Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Controlcited by 104
  22. 2018Unsupervised Control Through Non-Parametric Discriminative Rewardscited by 102
  23. 2020Automatic Curriculum Learning through Value Disagreementcited by 86
  24. 2019Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discoverycited by 65

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 8; 2025: 6; 2026: 1.

23 papers.

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  1. 2024DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planningcited by 357
  2. 2025π*0.6: a VLA That Learns From Experiencecited by 280
  3. 2023Affordances from Human Videos as a Versatile Representation for Roboticscited by 221
  4. 2023Structured World Models from Human Videoscited by 144
  5. 2024Open-TeleVision: Teleoperation with Immersive Active Visual Feedbackcited by 137
  6. 2023OMNI: Open-endedness via Models of human Notions of Interestingnesscited by 66
  7. 2025A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learningcited by 66
  8. 2025AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Controlcited by 47
  9. 2024Adaptive Mobile Manipulation for Articulated Objects In the Open Worldcited by 30
  10. 2024Continuously Improving Mobile Manipulation with Autonomous Real-World RLcited by 20
  11. 2023Harmonic Mobile Manipulationcited by 16
  12. 2023DEFT: Dexterous Fine-Tuning for Real-World Hand Policiescited by 15
11 more
  1. 2024Agent Alignment in Evolving Social Normscited by 14
  2. 2023Efficient RL via Disentangled Environment and Agent Representationscited by 11
  3. 2023Towards Generalist Robots: A Promising Paradigm via Generative Simulationcited by 9
  4. 2024World Models for General Surgical Graspingcited by 9
  5. 2025WoMAP: World Models For Embodied Open-Vocabulary Object Localizationcited by 8
  6. 2026ALOE: Action-Level Off-Policy Evaluation for Vision-Language-Action Model Post-Trainingcited by 7
  7. 2025Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learningcited by 4
  8. 2023Goal-conditioned Offline Planning from Curious Explorationcited by 3
  9. 2024Reward Bound for Behavioral Guarantee of Model-based Planning Agentscited by 2
  10. 2024Ego-Foresight: Self-supervised Learning of Agent-Aware Representations for Improved RLcited by 0
  11. 2025Focusing Robot Open-Ended Reinforcement Learning Through Users' Purposescited by 0