# Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback

arXiv: https://arxiv.org/abs/2409.12273 · HTML: https://mlsanity.com/paper/2409.12273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 18 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 16 of 16)

- [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)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects](https://mlsanity.com/paper/1809.10790/) (2018)
- [Active Domain Randomization](https://mlsanity.com/paper/1904.04762/) (2019)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Grasp Pose Detection in Point Clouds](https://mlsanity.com/paper/1706.09911/) (2017)
- [Self-supervised 6D Object Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/1909.10159/) (2019)
- [The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?](https://mlsanity.com/paper/1710.05512/) (2017)
- [Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision](https://mlsanity.com/paper/1806.09266/) (2018)
- [Deep Predictive Policy Training using Reinforcement Learning](https://mlsanity.com/paper/1703.00727/) (2017)
- [Learning Continuous 3D Reconstructions for Geometrically Aware Grasping](https://mlsanity.com/paper/1910.00983/) (2019)
- [ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction](https://mlsanity.com/paper/2210.06887/) (2022)
- [GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping](https://mlsanity.com/paper/1912.13470/) (2019)

## Built on by (most cited first, 1 of 1)

- [Phase-Gated Constrained Reinforcement Learning for End-to-End Capture and Detumbling of Non-Cooperative Targets](https://mlsanity.com/paper/2610.15491/) (2026)

## Abstract (arXiv)

Traditional control methods effectively manage robot operations using models like motion equations but face challenges with issues of contact and friction, leading to unstable and imprecise controllers that often require manual tweaking. Reinforcement learning, however, has developed as a capable solution for developing robust robot controllers that excel in handling contact-related challenges. In this work, we introduce a deep reinforcement learning approach to tackle the soft-capture phase for free-floating moving targets, mainly space debris, amidst noisy data. Our findings underscore the crucial role of tactile sensors, even during the soft-capturing phase. By employing deep reinforcement learning, we eliminate the need for manual feature design, simplifying the problem and allowing the robot to learn soft-capture strategies through trial and error. To facilitate effective learning of the approach phase, we have crafted a specialized reward function that offers clear and insightful feedback to the agent. Our method is trained entirely within the simulation environment, eliminating the need for direct demonstrations or prior knowledge of the task. The developed control policy shows promising results, highlighting the necessity of using tactile sensor information. The code and simulation results are available at Soft_Capture_Tactile repo.