# Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot

arXiv: https://arxiv.org/abs/2302.09772 · HTML: https://mlsanity.com/paper/2302.09772/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 70 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 30 of 39)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [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)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Deep Reinforcement Learning at the Edge of the Statistical Precipice](https://mlsanity.com/paper/2108.13264/) (2021)
- [CURL: Contrastive Unsupervised Representations for Reinforcement Learning](https://mlsanity.com/paper/2004.04136/) (2020)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [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)
- [Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels](https://mlsanity.com/paper/2004.13649/) (2020)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Offline Reinforcement Learning with Fisher Divergence Critic Regularization](https://mlsanity.com/paper/2103.08050/) (2021)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://mlsanity.com/paper/1804.02717/) (2018)
- [Goal-conditioned Imitation Learning](https://mlsanity.com/paper/1906.05838/) (2019)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [Reinforcement and Imitation Learning for Diverse Visuomotor Skills](https://mlsanity.com/paper/1802.09564/) (2018)
- [RRL: Resnet as representation for Reinforcement Learning](https://mlsanity.com/paper/2107.03380/) (2021)
- [Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost](https://mlsanity.com/paper/1810.06045/) (2018)
- [Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation](https://mlsanity.com/paper/2203.13251/) (2022)
- [Hierarchical Few-Shot Imitation with Skill Transition Models](https://mlsanity.com/paper/2107.08981/) (2021)
- [SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning](https://mlsanity.com/paper/2108.13035/) (2021)
- [Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery](https://mlsanity.com/paper/2109.02323/) (2021)

## Built on by (most cited first, 23 of 23)

- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [General-purpose foundation models for increased autonomy in robot-assisted surgery](https://mlsanity.com/paper/2401.00678/) (2024)
- [Value-Informed Skill Chaining for Policy Learning of Long-Horizon Tasks with Surgical Robot](https://mlsanity.com/paper/2307.16503/) (2023)
- [World Models for General Surgical Grasping](https://mlsanity.com/paper/2405.17940/) (2024)
- [Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots](https://mlsanity.com/paper/2310.04676/) (2023)
- [Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments](https://mlsanity.com/paper/2310.08841/) (2023)
- [Multi-objective Cross-task Learning via Goal-conditioned GPT-based Decision Transformers for Surgical Robot Task Automation](https://mlsanity.com/paper/2405.18757/) (2024)
- [Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement Learning](https://mlsanity.com/paper/2309.00837/) (2023)
- [SimEndoGS: Efficient Data-driven Scene Simulation using Robotic Surgery Videos via Physics-embedded 3D Gaussians](https://mlsanity.com/paper/2405.00956/) (2024)
- [Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets](https://mlsanity.com/paper/2407.19714/) (2024)
- [Surgical Task Automation Using Actor-Critic Frameworks and Self-Supervised Imitation Learning](https://mlsanity.com/paper/2409.02724/) (2024)
- [Realistic Surgical Simulation from Monocular Videos](https://mlsanity.com/paper/2412.02359/) (2024)
- [FF-SRL: High Performance GPU-Based Surgical Simulation For Robot Learning](https://mlsanity.com/paper/2503.18616/) (2025)
- [Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems](https://mlsanity.com/paper/2307.03853/) (2023)
- [Signal Temporal Logic-Guided Apprenticeship Learning](https://mlsanity.com/paper/2311.05084/) (2023)
- [dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale](https://mlsanity.com/paper/2503.05646/) (2025)
- [Visuomotor Grasping with World Models for Surgical Robots](https://mlsanity.com/paper/2508.11200/) (2025)
- [SurgIRL: Towards Life-Long Learning for Surgical Automation by Incremental Reinforcement Learning](https://mlsanity.com/paper/2409.15651/) (2024)
- [Boosting deep Reinforcement Learning using pretraining with Logical Options](https://mlsanity.com/paper/2603.06565/) (2026)
- [SSP: Safety-guaranteed Surgical Policy via Joint Optimization of Behavioral and Spatial Constraints](https://mlsanity.com/paper/2603.07032/) (2026)
- [Match or Replay: Self Imitating Proximal Policy Optimization](https://mlsanity.com/paper/2603.27515/) (2026)
- [E2DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation](https://mlsanity.com/paper/2605.00159/) (2026)
- [SurgVIL: Scaling Surgical Robot Imitation Learning with Open-source Surgical Videos](https://mlsanity.com/paper/2608.16058/) (2026)

## Abstract (arXiv)

Task automation of surgical robot has the potentials to improve surgical efficiency. Recent reinforcement learning (RL) based approaches provide scalable solutions to surgical automation, but typically require extensive data collection to solve a task if no prior knowledge is given. This issue is known as the exploration challenge, which can be alleviated by providing expert demonstrations to an RL agent. Yet, how to make effective use of demonstration data to improve exploration efficiency still remains an open challenge. In this work, we introduce Demonstration-guided EXploration (DEX), an efficient reinforcement learning algorithm that aims to overcome the exploration problem with expert demonstrations for surgical automation. To effectively exploit demonstrations, our method estimates expert-like behaviors with higher values to facilitate productive interactions, and adopts non-parametric regression to enable such guidance at states unobserved in demonstration data. Extensive experiments on 10 surgical manipulation tasks from SurRoL, a comprehensive surgical simulation platform, demonstrate significant improvements in the exploration efficiency and task success rates of our method. Moreover, we also deploy the learned policies to the da Vinci Research Kit (dVRK) platform to show the effectiveness on the real robot. Code is available at https://github.com/med-air/DEX.