# Watch and Match: Supercharging Imitation with Regularized Optimal Transport

arXiv: https://arxiv.org/abs/2206.15469 · HTML: https://mlsanity.com/paper/2206.15469/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 43 of its 81 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 30 of 43)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction](https://mlsanity.com/paper/1906.00949/) (2019)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [AWAC: Accelerating Online Reinforcement Learning with Offline Datasets](https://mlsanity.com/paper/2006.09359/) (2020)
- [Behavior Regularized Offline Reinforcement Learning](https://mlsanity.com/paper/1911.11361/) (2019)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning](https://mlsanity.com/paper/2107.09645/) (2021)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research](https://mlsanity.com/paper/1802.09464/) (2018)
- [Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning](https://mlsanity.com/paper/2002.08396/) (2020)
- [Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning](https://mlsanity.com/paper/1809.02925/) (2018)
- [A Divergence Minimization Perspective on Imitation Learning Methods](https://mlsanity.com/paper/1911.02256/) (2019)
- [Generative Adversarial Imitation from Observation](https://mlsanity.com/paper/1807.06158/) (2018)
- [OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning](https://mlsanity.com/paper/2010.13611/) (2020)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [Primal Wasserstein Imitation Learning](https://mlsanity.com/paper/2006.04678/) (2020)

## Built on by (most cited first, 30 of 67)

- [A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges](https://mlsanity.com/paper/2309.02473/) (2023)
- [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023)
- [Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations](https://mlsanity.com/paper/2303.01497/) (2023)
- [Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model](https://mlsanity.com/paper/2412.13630/) (2024)
- [Imitation Bootstrapped Reinforcement Learning](https://mlsanity.com/paper/2311.02198/) (2023)
- [Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons](https://mlsanity.com/paper/2603.02115/) (2026)
- [Holo-Dex: Teaching Dexterity with Immersive Mixed Reality](https://mlsanity.com/paper/2210.06463/) (2022)
- [QUAR-VLA: Vision-Language-Action Model for Quadruped Robots](https://mlsanity.com/paper/2312.14457/) (2023)
- [Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans](https://mlsanity.com/paper/2312.00775/) (2023)
- [Vision-based Manipulation from Single Human Video with Open-World Object Graphs](https://mlsanity.com/paper/2405.20321/) (2024)
- [See to Touch: Learning Tactile Dexterity through Visual Incentives](https://mlsanity.com/paper/2309.12300/) (2023)
- [Rank2Reward: Learning Shaped Reward Functions from Passive Video](https://mlsanity.com/paper/2404.14735/) (2024)
- [Adaptive Mobile Manipulation for Articulated Objects In the Open World](https://mlsanity.com/paper/2401.14403/) (2024)
- [Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections](https://mlsanity.com/paper/2506.16685/) (2025)
- [Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewards](https://mlsanity.com/paper/2410.23289/) (2024)
- [Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey](https://mlsanity.com/paper/2305.05080/) (2023)
- [For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal](https://mlsanity.com/paper/2304.04591/) (2023)
- [Planning for Sample Efficient Imitation Learning](https://mlsanity.com/paper/2210.09598/) (2022)
- [Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own](https://mlsanity.com/paper/2310.02635/) (2023)
- [Privileged Sensing Scaffolds Reinforcement Learning](https://mlsanity.com/paper/2405.14853/) (2024)
- [So You Think You Can Scale Up Autonomous Robot Data Collection?](https://mlsanity.com/paper/2411.01813/) (2024)
- [A Smooth Sea Never Made a Skilled SAILOR: Robust Imitation via Learning to Search](https://mlsanity.com/paper/2506.05294/) (2025)
- [Visual Imitation Learning with Patch Rewards](https://mlsanity.com/paper/2302.00965/) (2023)
- [P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of Robot Policies](https://mlsanity.com/paper/2412.06784/) (2024)
- [Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation](https://mlsanity.com/paper/2301.11741/) (2023)
- [XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations](https://mlsanity.com/paper/2511.02776/) (2025)
- [What Matters to You? Towards Visual Representation Alignment for Robot Learning](https://mlsanity.com/paper/2310.07932/) (2023)
- [PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement](https://mlsanity.com/paper/2411.17764/) (2024)
- [Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directions](https://mlsanity.com/paper/2502.15336/) (2025)
- [Reinforcement Learning via Implicit Imitation Guidance](https://mlsanity.com/paper/2506.07505/) (2025)

## Abstract (arXiv)

Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems. Current state-of-the-art algorithms often use inverse reinforcement learning (IRL), where given a set of expert demonstrations, an agent alternatively infers a reward function and the associated optimal policy. However, such IRL approaches often require substantial online interactions for complex control problems. In this work, we present Regularized Optimal Transport (ROT), a new imitation learning algorithm that builds on recent advances in optimal transport based trajectory-matching. Our key technical insight is that adaptively combining trajectory-matching rewards with behavior cloning can significantly accelerate imitation even with only a few demonstrations. Our experiments on 20 visual control tasks across the DeepMind Control Suite, the OpenAI Robotics Suite, and the Meta-World Benchmark demonstrate an average of 7.8X faster imitation to reach 90% of expert performance compared to prior state-of-the-art methods. On real-world robotic manipulation, with just one demonstration and an hour of online training, ROT achieves an average success rate of 90.1% across 14 tasks.