mlsanityData to 7 Oct 2026

BAKU: An Efficient Transformer for Multi-Task Policy Learning

arXiv 2406.07539 ↗First posted 11 Jun 2024cs.ROcited by 80

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Cited by 23 in Vision-language-action models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Training generalist agents capable of solving diverse tasks is challenging, often requiring large datasets of expert demonstrations. This is particularly problematic in robotics, where each data point requires physical execution of actions in the real world. Thus, there is a pressing need for architectures that can effectively leverage the available training data. In this work, we present BAKU, a simple transformer architecture that enables efficient learning of multi-task robot policies. BAKU builds upon recent advancements in offline imitation learning and meticulously combines observation trunks, action chunking, multi-sensory observations, and action heads to substantially improve upon prior work. Our experiments on 129 simulated tasks across LIBERO, Meta-World suite, and the Deepmind Control suite exhibit an overall 18% absolute improvement over RT-1 and MT-ACT, with a 36% improvement on the harder LIBERO benchmark. On 30 real-world manipulation tasks, given an average of just 17 demonstrations per task, BAKU achieves a 91% success rate. Videos of the robot are best viewed at https://baku-robot.github.io/.

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 16; 2025: 45; 2026: 19.

80 papers.

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  1. 2025SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Modelcited by 502
  2. 2024DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planningcited by 357
  3. 2024RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulationcited by 166
  4. 2024Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformerscited by 145
  5. 2025Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Bettercited by 142
  6. 2025Vision-Language-Action Models for Robotics: A Review Towards Real-World Applicationscited by 82
  7. 2025Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challengescited by 72
  8. 2024Moto: Latent Motion Token as the Bridging Language for Learning Robot Manipulation from Videoscited by 65
  9. 2025FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policiescited by 56
  10. 2025Pure Vision Language Action (VLA) Models: A Comprehensive Surveycited by 46
  11. 2024Robot Utility Models: General Policies for Zero-Shot Deployment in New Environmentscited by 43
  12. 2024DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Controlcited by 43
38 more
  1. 2024From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessonscited by 42
  2. 2024STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learningcited by 42
  3. 2025A Survey on Efficient Vision-Language-Action Modelscited by 34
  4. 2025Learning Long-Context Diffusion Policies via Past-Token Predictioncited by 28
  5. 2025EgoZero: Robot Learning from Smart Glassescited by 28
  6. 2024Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewardscited by 25
  7. 2025GeoAware-VLA: Implicit Geometry Aware Vision-Language-Action Modelcited by 24
  8. 2024AnySkin: Plug-and-play Skin Sensing for Robotic Touchcited by 22
  9. 2025Latent Action Pretraining Through World Modelingcited by 21
  10. 2025AMPLIFY: Actionless Motion Priors for Robot Learning from Videoscited by 18
  11. 2024P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of Robot Policiescited by 16
  12. 2025Curating Demonstrations using Online Experiencecited by 16
  13. 2025Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learningcited by 16
  14. 2025Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulationcited by 15
  15. 2025SwitchVLA: Execution-Aware Task Switching for Vision-Language-Action Modelscited by 15
  16. 2025Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrationscited by 14
  17. 2025Touch begins where vision ends: Generalizable policies for contact-rich manipulationcited by 13
  18. 2024REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environmentscited by 12
  19. 2024Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skinscited by 11
  20. 2025BFA: Best-Feature-Aware Fusion for Multi-View Fine-grained Manipulationcited by 10
  21. 2025MTIL: Encoding Full History with Mamba for Temporal Imitation Learningcited by 10
  22. 2025Vintix: Action Model via In-Context Reinforcement Learningcited by 8
  23. 2025ACT-JEPA: Novel Joint-Embedding Predictive Architecture for Efficient Policy Representation Learningcited by 7
  24. 2025Feel the Force: Contact-Driven Learning from Humanscited by 7
  25. 2025Object-Centric Representations Improve Policy Generalization in Robot Manipulationcited by 6
  26. 2026Hierarchical Latent Action Modelcited by 6
  27. 2025Text-to-Decision Agent: Offline Meta-Reinforcement Learning from Natural Language Supervisioncited by 5
  28. 2025ReFineVLA: Reasoning-Aware Teacher-Guided Transfer Fine-Tuningcited by 5
  29. 2025COLLAGE: Adaptive Fusion-based Retrieval for Augmented Policy Learningcited by 5
  30. 2025Learning to See and Act: Task-Aware Virtual View Exploration for Robotic Manipulationcited by 5
  31. 2025SOE: Sample-Efficient Robot Policy Self-Improvement via On-Manifold Explorationcited by 5
  32. 2025eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructurescited by 4
  33. 2025Self-evolved Imitation Learning in Simulated Worldcited by 4
  34. 2025An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challengescited by 4
  35. 2025CIVIL: Causal and Intuitive Visual Imitation Learningcited by 3
  36. 2025Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Spacecited by 3
  37. 2025Is an object-centric representation beneficial for robotic manipulation ?cited by 3
  38. 2025LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrationscited by 3

50 most cited of 80 All 80 (JSON)

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  1. 2020Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
  2. 2020Scalable Multi-Task Imitation Learning with Autonomous Improvement
  3. 2022RT-1: Robotics Transformer for Real-World Control at Scale
  4. 2024AdaDemo: Data-Efficient Demonstration Expansion for Generalist Robotic Agent
  5. 2024Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames
  6. 2023RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
  7. 2024Active Fine-Tuning of Multi-Task Policies
  8. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning