mlsanityData to 14 Oct 2026

Precision-Focused Reinforcement Learning Model for Robotic Object Pushing

arXiv 2411.08622 ↗First posted 13 Nov 2024cs.RO cs.AIcited by 5

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

Abstract (arXiv)

Non-prehensile manipulation, such as pushing objects to a desired target position, is an important skill for robots to assist humans in everyday situations. However, the task is challenging due to the large variety of objects with different and sometimes unknown physical properties, such as shape, size, mass, and friction. This can lead to the object overshooting its target position, requiring fast corrective movements of the robot around the object, especially in cases where objects need to be precisely pushed. In this paper, we improve the state-of-the-art by introducing a new memory-based vision-proprioception RL model to push objects more precisely to target positions using fewer corrective movements.

Key facts. Cited by 5 papers 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 21 of its 38 references that are in the map. First posted on arXiv 13 Nov 2024.

Builds on

21 of 38 references.

References · 21

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  4. 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,061
  5. 2016OpenAI Gymcited by 1,736
  6. 2017Hindsight Experience Replaycited by 1,136
  7. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  8. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 260
  9. 2018Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learningcited by 128
  10. 2021panda-gym: Open-source goal-conditioned environments for robotic learningcited by 59
  11. 2022Visuo-Tactile Transformers for Manipulationcited by 47
  12. 2018Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncingcited by 36
9 more
  1. 2018Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical systemcited by 17
  2. 2019Let's Push Things Forward: A Survey on Robot Pushingcited by 14
  3. 2023Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environmentcited by 13
  4. 2018A Data-Efficient Approach to Precise and Controlled Pushingcited by 7
  5. 2017Learning to Singulate Objects using a Push Proposal Networkcited by 5
  6. 2020COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushingcited by 4
  7. 2024UNO Push: Unified Nonprehensile Object Pushing via Non-Parametric Estimation and Model Predictive Controlcited by 4
  8. 2020Self-Adapting Recurrent Models for Object Pushing from Learning in Simulationcited by 3
  9. 2022Learning Goal-Oriented Non-Prehensile Pushing in Cluttered Scenescited by 3

Built on by

Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Can Context Bridge the Reality Gap? Sim-to-Real Transfer of Context-Aware Policiescited by 4
  2. 2025SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Taskscited by 3
  3. 2025End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation Systemcited by 2
  4. 2025Latent Representations for Visual Proprioception in Inexpensive Robotscited by 0
  5. 2025MagBotSim: Physics-Based Simulation and Reinforcement Learning Environments for Magnetic Roboticscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning
  2. 2024Learning Goal-Directed Object Pushing in Cluttered Scenes With Location-Based Attention
  3. 2025FLEX: A Framework for Learning Robot-Agnostic Force-based Skills Involving Sustained Contact Object Manipulation
  4. 2026Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
  5. 2024Harnessing the Synergy between Pushing, Grasping, and Throwing to Enhance Object Manipulation in Cluttered Scenarios ↗
  6. 2025Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials ↗
  7. 2024Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
  8. 2025Reinforcement Learning for Robotic Safe Control with Force Sensing