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

arXiv: https://arxiv.org/abs/2311.09555 · HTML: https://mlsanity.com/paper/2311.09555/

## Key facts

- Cited by 15 papers in the map (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 10 of its 27 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks](https://mlsanity.com/paper/1906.08880/) (2019)
- [Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control](https://mlsanity.com/paper/1811.03759/) (2018)
- [An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications](https://mlsanity.com/paper/2203.08619/) (2022)
- [Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed](https://mlsanity.com/paper/2206.10289/) (2022)
- [Time Series Motion Generation Considering Long Short-Term Motion](https://mlsanity.com/paper/1909.03755/) (2019)
- [Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning](https://mlsanity.com/paper/2103.05912/) (2021)

## Built on by (most cited first, 15 of 15)

- [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024)
- [Mamba Policy: Towards Efficient 3D Diffusion Policy with Hybrid Selective State Models](https://mlsanity.com/paper/2409.07163/) (2024)
- [ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer](https://mlsanity.com/paper/2401.16653/) (2024)
- [Adaptive Wiping: Adaptive contact-rich manipulation through few-shot imitation learning with Force-Torque feedback and pre-trained object representations](https://mlsanity.com/paper/2505.06451/) (2025)
- [Variable-Frequency Imitation Learning for Variable-Speed Motion](https://mlsanity.com/paper/2411.12310/) (2024)
- [Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning](https://mlsanity.com/paper/2412.03252/) (2024)
- [Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research](https://mlsanity.com/paper/2506.12312/) (2025)
- [Design and Experimental Validation of Sensorless 4-Channel Bilateral Teleoperation for Low-Cost Manipulators](https://mlsanity.com/paper/2507.06174/) (2025)
- [Imitation Learning Inputting Image Feature to Each Layer of Neural Network](https://mlsanity.com/paper/2401.09691/) (2024)
- [Learning Gentle Grasping from Human-Free Force Control Demonstration](https://mlsanity.com/paper/2409.10371/) (2024)
- [Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning](https://mlsanity.com/paper/2411.12255/) (2024)
- [Imitation Learning Based on Disentangled Representation Learning of Behavioral Characteristics](https://mlsanity.com/paper/2509.04737/) (2025)
- [Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning](https://mlsanity.com/paper/2604.16850/) (2026)
- [Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique](https://mlsanity.com/paper/2602.06620/) (2026)
- [VFILC: Accurate Frequency Extrapolations in Imitation Learning via Sampling Frequency ILC](https://mlsanity.com/paper/2606.20056/) (2026)

## 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.