mlsanityData to 14 Oct 2026

Variable-Friction In-Hand Manipulation for Arbitrary Objects via Diffusion-Based Imitation Learning

arXiv 2503.02738 ↗First posted 4 Mar 2025cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

Dexterous in-hand manipulation (IHM) for arbitrary objects is challenging due to the rich and subtle contact process. Variable-friction manipulation is an alternative approach to dexterity, previously demonstrating robust and versatile 2D IHM capabilities with only two single-joint fingers. However, the hard-coded manipulation methods for variable friction hands are restricted to regular polygon objects and limited target poses, as well as requiring the policy to be tailored for each object. This paper proposes an end-to-end learning-based manipulation method to achieve arbitrary object manipulation for any target pose on real hardware, with minimal engineering efforts and data collection. The method features a diffusion policy-based imitation learning method with co-training from simulation and a small amount of real-world data. With the proposed framework, arbitrary objects including polygons and non-polygons can be precisely manipulated to reach arbitrary goal poses within 2 hours of training on an A100 GPU and only 1 hour of real-world data collection. The precision is higher than previous customized object-specific policies, achieving an average success rate of 71.3% with average pose error being 2.676 mm and 1.902 degrees.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 26 of its 34 references that are in the map. First posted on arXiv 4 Mar 2025.

Builds on

26 of 34 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning3 Jul
  2. 2024Tilde: Teleoperation for Dexterous In-Hand Manipulation Learning with a DeltaHand29 May
  3. 2024Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation4 Jan
  4. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware23 Apr
  5. 2022From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation26 Apr
  6. 2022Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation24 Mar

Other references · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  5. 2024Octo: An Open-Source Generalist Robot Policycited by 1,484
  6. 2017Hindsight Experience Replaycited by 1,136
  7. 2019Solving Rubik's Cube with a Robot Handcited by 734
  8. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 715
  9. 2018Learning Dexterous In-Hand Manipulationcited by 687
  10. 2019Deep Dynamics Models for Learning Dexterous Manipulationcited by 206
  11. 2021A System for General In-Hand Object Re-Orientationcited by 165
  12. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challengescited by 148
8 more
  1. 2022Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapescited by 101
  2. 2023Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learningcited by 100
  3. 2018Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Costcited by 84
  4. 2020Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamicscited by 21
  5. 2023Learning a Universal Human Prior for Dexterous Manipulation from Human Preferencecited by 10
  6. 2020Design and Control of Roller Grasper V2 for In-Hand Manipulationcited by 6
  7. 2020Region-Based Planning for 3D Within-Hand-Manipulation via Variable Friction Robot Fingers and Extrinsic Contactscited by 1
  8. 2022E-TRoll: Tactile Sensing and Classification via A Simple Robotic Gripper for Extended Rolling Manipulationscited by 1

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  1. 2023Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum
  2. 2025Dexterous Manipulation through Imitation Learning: A Survey
  3. 2024On the Feasibility of A Mixed-Method Approach for Solving Long Horizon Task-Oriented Dexterous Manipulation
  4. 2020Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning
  5. 2025Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion
  6. 2023In-Hand Re-grasp Manipulation with Passive Dynamic Actions via Imitation Learning
  7. 2022Learning Generalizable Dexterous Manipulation from Human Grasp Affordance
  8. 2021DexMV: Imitation Learning for Dexterous Manipulation from Human Videos