mlsanityData to 14 Oct 2026

Learning Tactile Insertion in the Real World

arXiv 2405.00383 ↗First posted 1 May 2024cs.ROcited by 1

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Humans have exceptional tactile sensing capabilities, which they can leverage to solve challenging, partially observable tasks that cannot be solved from visual observation alone. Research in tactile sensing attempts to unlock this new input modality for robots. Lately, these sensors have become cheaper and, thus, widely available. At the same time, the question of how to integrate them into control loops is still an active area of research, with central challenges being partial observability and the contact-rich nature of manipulation tasks. In this study, we propose to use Reinforcement Learning to learn an end-to-end policy, mapping directly from tactile sensor readings to actions. Specifically, we use Dreamer-v3 on a challenging, partially observable robotic insertion task with a Franka Research 3, both in simulation and on a real system. For the real setup, we built a robotic platform capable of resetting itself fully autonomously, allowing for extensive training runs without human supervision. Our preliminary results indicate that Dreamer is capable of utilizing tactile inputs to solve robotic manipulation tasks in simulation and reality. Furthermore, we find that providing the robot with tactile feedback generally improves task performance, though, in our setup, we do not yet include other sensing modalities. In the future, we plan to utilize our platform to evaluate a wide range of other Reinforcement Learning algorithms on tactile tasks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 25 of its 33 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

25 of 33 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
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  11. 2019Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Modelcited by 231
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13 more
  1. 2020DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulationcited by 167
  2. 2019CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicitycited by 95
  3. 2021DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representationscited by 74
  4. 2021Temporal Predictive Coding For Model-Based Planning In Latent Spacecited by 55
  5. 2021Taxim: An Example-based Simulation Model for GelSight Tactile Sensorscited by 43
  6. 2021ShapeMap 3-D: Efficient shape mapping through dense touch and visioncited by 31
  7. 2017Active Clothing Material Perception using Tactile Sensing and Deep Learningcited by 30
  8. 2021Model-Based Reinforcement Learning via Latent-Space Collocationcited by 28
  9. 2021Jerk-limited Real-time Trajectory Generation with Arbitrary Target Statescited by 16
  10. 2022Active Exploration for Robotic Manipulationcited by 10
  11. 2024What Matters for Active Texture Recognition With Vision-Based Tactile Sensorscited by 7
  12. 2023Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learningcited by 6
  13. 2019Learning Latent State Spaces for Planning through Reward Predictioncited by 4

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Privileged observations enable rapid and reliable policy discovery directly in the physical worldcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2024Sim2Real Manipulation on Unknown Objects with Tactile-based Reinforcement Learning
  2. 2026Tactile-Guided Robot Learning: From Representation Learning to Foundation Models ↗
  3. 2026OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework ↗
  4. 2025Enhancing Tactile-based Reinforcement Learning for Robotic Control
  5. 2026Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies ↗
  6. 2026TacCoRL: Integrating Tactile Feedback into VLA via Simulation
  7. 2025The Role of Tactile Sensing for Learning Reach and Grasp
  8. 2024Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing