mlsanityData to 7 Oct 2026

Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning

arXiv 2311.09555 ↗First posted 16 Nov 2023cs.RO cs.SY eess.SYcited by 15

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

Abstract (arXiv)

Object grasping is an important ability required for various robot tasks. In particular, tasks that require precise force adjustments during operation, such as grasping an unknown object or using a grasped tool, are difficult for humans to program in advance. Recently, AI-based algorithms that can imitate human force skills have been actively explored as a solution. In particular, bilateral control-based imitation learning achieves human-level motion speeds with environmental adaptability, only requiring human demonstration and without programming. However, owing to hardware limitations, its grasping performance remains limited, and tasks that involves grasping various objects are yet to be achieved. Here, we developed a cross-structure hand to grasp various objects. We experimentally demonstrated that the integration of bilateral control-based imitation learning and the cross-structure hand is effective for grasping various objects and harnessing tools.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 27 references that are in the map. First posted on arXiv 16 Nov 2023.

Builds on

10 of 27 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed21 Jun
  2. 2022An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications16 Mar
  3. 2021Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning10 Mar

Other references · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 699
  4. 2020Transporter Networks: Rearranging the Visual World for Robotic Manipulationcited by 342
  5. 2019Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Taskscited by 56
  6. 2018Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Controlcited by 21
  7. 2019Time Series Motion Generation Considering Long Short-Term Motioncited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 4; 2026: 3.

15 papers.

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  1. 2024Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformercited by 27
  2. 2024Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Modelscited by 22
  3. 2024ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformercited by 10
  4. 2025Adaptive Wiping: Adaptive contact-rich manipulation through few-shot imitation learning with Force-Torque feedback and pre-trained object representationscited by 3
  5. 2024Variable-Frequency Imitation Learning for Variable-Speed Motioncited by 2
  6. 2024Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learningcited by 2
  7. 2025Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Researchcited by 2
  8. 2025Design and Experimental Validation of Sensorless 4-Channel Bilateral Teleoperation for Low-Cost Manipulatorscited by 2
  9. 2024Imitation Learning Inputting Image Feature to Each Layer of Neural Networkcited by 1
  10. 2024Learning Gentle Grasping from Human-Free Force Control Demonstrationcited by 1
  11. 2024Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learningcited by 1
  12. 2025Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristicscited by 1
3 more
  1. 2026Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learningcited by 1
  2. 2026Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Techniquecited by 0
  3. 2026VFILC: Accurate Frequency Extrapolations in Imitation Learning via Sampling Frequency ILCcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2019Imitation Learning Based on Bilateral Control for Human-Robot Cooperation
  2. 2025Dexterous Manipulation through Imitation Learning: A Survey
  3. 2024ALPHA-α and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System
  4. 2021DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
  5. 2024On the Feasibility of A Mixed-Method Approach for Solving Long Horizon Task-Oriented Dexterous Manipulation
  6. 2021Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot
  7. 2025Variable-Friction In-Hand Manipulation for Arbitrary Objects via Diffusion-Based Imitation Learning
  8. 2022From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation