mlsanityData to 7 Oct 2026

Continuously Improving Mobile Manipulation with Autonomous Real-World RL

arXiv 2409.20568 ↗First posted 30 Sep 2024cs.RO cs.AI cs.CVcited by 20

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Abstract (arXiv)

We present a fully autonomous real-world RL framework for mobile manipulation that can learn policies without extensive instrumentation or human supervision. This is enabled by 1) task-relevant autonomy, which guides exploration towards object interactions and prevents stagnation near goal states, 2) efficient policy learning by leveraging basic task knowledge in behavior priors, and 3) formulating generic rewards that combine human-interpretable semantic information with low-level, fine-grained observations. We demonstrate that our approach allows Spot robots to continually improve their performance on a set of four challenging mobile manipulation tasks, obtaining an average success rate of 80% across tasks, a 3-4 improvement over existing approaches. Videos can be found at https://continual-mobile-manip.github.io/

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 49 of its 54 references that are in the map. First posted on arXiv 30 Sep 2024.

Builds on

49 of 54 references.

References · 49

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  2. 2023Segment Anythingcited by 8,158
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  4. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
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  11. 2020Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixelscited by 573
  12. 2024Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperationcited by 377
37 more
  1. 2022DayDreamer: World Models for Physical Robot Learningcited by 372
  2. 2023Efficient Online Reinforcement Learning with Offline Datacited by 332
  3. 2023Language to Rewards for Robotic Skill Synthesiscited by 307
  4. 2021Randomized Ensembled Double Q-Learning: Learning Fast Without a Modelcited by 278
  5. 2021MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scalecited by 226
  6. 2023TidyBot: Personalized Robot Assistance with Large Language Modelscited by 219
  7. 2019Deep Dynamics Models for Learning Dexterous Manipulationcited by 200
  8. 2019Skew-Fit: State-Covering Self-Supervised Reinforcement Learningcited by 171
  9. 2023Extreme Parkour with Legged Robotscited by 120
  10. 2019End-to-End Robotic Reinforcement Learning without Reward Engineeringcited by 115
  11. 2020The Ingredients of Real-World Robotic Reinforcement Learningcited by 111
  12. 2023Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learningcited by 111
  13. 2019AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videoscited by 105
  14. 2020Learning to Walk in the Real World with Minimal Human Effortcited by 103
  15. 2022Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deploymentcited by 101
  16. 2023On Bringing Robots Homecited by 101
  17. 2021Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Interventioncited by 90
  18. 2018Robot Learning in Homes: Improving Generalization and Reducing Dataset Biascited by 75
  19. 2018Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definitioncited by 66
  20. 2023Vision-Language Models as a Source of Rewardscited by 65
  21. 2021Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real Worldcited by 64
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  23. 2022Multi-skill Mobile Manipulation for Object Rearrangementcited by 46
  24. 2023ASC: Adaptive Skill Coordination for Robotic Mobile Manipulationcited by 38
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  26. 2021Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulationcited by 30
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 10.

20 papers.

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  1. 2025π*0.6: a VLA That Learns From Experiencecited by 280
  2. 2025ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policycited by 139
  3. 2025A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learningcited by 66
  4. 2025MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulationcited by 34
  5. 2026Large Reward Models: Generalizable Online Robot Reward Generation with Vision-Language Modelscited by 11
  6. 2026Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMscited by 10
  7. 2025UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policiescited by 9
  8. 2026From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuningcited by 8
  9. 2024On-Robot Reinforcement Learning with Goal-Contrastive Rewardscited by 7
  10. 2025SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Trainingcited by 7
  11. 2025SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulationcited by 5
  12. 2026TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulationcited by 5
8 more
  1. 2026Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worldscited by 2
  2. 2025Bipedalism for Quadrupedal Robots: Versatile Loco-Manipulation through Risk-Adaptive Reinforcement Learningcited by 1
  3. 2025Dynamic Object Masks as Goal Representations for Visual Goal-Conditioned Reinforcement Learningcited by 1
  4. 2026SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learningcited by 1
  5. 2026Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latencycited by 1
  6. 2026Incremental Residual Reinforcement Learning Toward Real-World Learning for Social Navigationcited by 0
  7. 2026Learning Process Rewards via Success Visitation Matching for Efficient RLcited by 0
  8. 2026Same Trajectory, Contradictory Rewards (ROBORMBENCH): Paraphrase Fragility in Vision Language Reward Modelscited by 0