# Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer

arXiv: https://arxiv.org/abs/2202.09574 · HTML: https://mlsanity.com/paper/2202.09574/

## Key facts

- Cited by 9 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 15 of its 30 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## 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, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Quantifying Attention Flow in Transformers](https://mlsanity.com/paper/2005.00928/) (2020)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation](https://mlsanity.com/paper/1707.03374/) (2017)
- [Deep Spatial Autoencoders for Visuomotor Learning](https://mlsanity.com/paper/1509.06113/) (2015)
- [One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://mlsanity.com/paper/1802.01557/) (2018)
- [Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations](https://mlsanity.com/paper/1912.04344/) (2019)
- [Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos](https://mlsanity.com/paper/2101.07241/) (2021)
- [Transformer-based deep imitation learning for dual-arm robot manipulation](https://mlsanity.com/paper/2108.00385/) (2021)
- [Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control](https://mlsanity.com/paper/1811.03759/) (2018)
- [Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation](https://mlsanity.com/paper/2102.01295/) (2021)
- [Recurrent Mixture Density Network for Spatiotemporal Visual Attention](https://mlsanity.com/paper/1603.08199/) (2016)
- [Third-party Evaluation of Robotic Hand Designs Using a Mechanical Glove](https://mlsanity.com/paper/2109.10501/) (2021)

## Built on by (most cited first, 9 of 9)

- [ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation](https://mlsanity.com/paper/2410.07554/) (2024)
- [Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation](https://mlsanity.com/paper/2203.09749/) (2022)
- [Dexterous Manipulation through Imitation Learning: A Survey](https://mlsanity.com/paper/2504.03515/) (2025)
- [AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons](https://mlsanity.com/paper/2503.03081/) (2025)
- [Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations](https://mlsanity.com/paper/2212.02126/) (2022)
- [Multi-task real-robot data with gaze attention for dual-arm fine manipulation](https://mlsanity.com/paper/2401.07603/) (2024)
- [Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning](https://mlsanity.com/paper/2505.18447/) (2025)
- [Guiding Attention in End-to-End Driving Models](https://mlsanity.com/paper/2405.00242/) (2024)
- [STAGE: STyle-controllable Action GEneration for personalized autonomous driving](https://mlsanity.com/paper/2607.29517/) (2026)

## Abstract (arXiv)

Deep imitation learning is promising for robot manipulation because it only requires demonstration samples. In this study, deep imitation learning is applied to tasks that require force feedback. However, existing demonstration methods have deficiencies; bilateral teleoperation requires a complex control scheme and is expensive, and kinesthetic teaching suffers from visual distractions from human intervention. This research proposes a new master-to-robot (M2R) policy transfer system that does not require robots for teaching force feedback-based manipulation tasks. The human directly demonstrates a task using a controller. This controller resembles the kinematic parameters of the robot arm and uses the same end-effector with force/torque (F/T) sensors to measure the force feedback. Using this controller, the operator can feel force feedback without a bilateral system. The proposed method can overcome domain gaps between the master and robot using gaze-based imitation learning and a simple calibration method. Furthermore, a Transformer is applied to infer policy from F/T sensory input. The proposed system was evaluated on a bottle-cap-opening task that requires force feedback.