# Precision-Focused Reinforcement Learning Model for Robotic Object Pushing

arXiv: https://arxiv.org/abs/2411.08622 · HTML: https://mlsanity.com/paper/2411.08622/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 21 of 21)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research](https://mlsanity.com/paper/1802.09464/) (2018)
- [Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning](https://mlsanity.com/paper/1803.09956/) (2018)
- [panda-gym: Open-source goal-conditioned environments for robotic learning](https://mlsanity.com/paper/2106.13687/) (2021)
- [Visuo-Tactile Transformers for Manipulation](https://mlsanity.com/paper/2210.00121/) (2022)
- [Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing](https://mlsanity.com/paper/1808.03246/) (2018)
- [Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system](https://mlsanity.com/paper/1803.10371/) (2018)
- [Let's Push Things Forward: A Survey on Robot Pushing](https://mlsanity.com/paper/1905.05138/) (2019)
- [Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment](https://mlsanity.com/paper/2302.10717/) (2023)
- [A Data-Efficient Approach to Precise and Controlled Pushing](https://mlsanity.com/paper/1807.09904/) (2018)
- [Learning to Singulate Objects using a Push Proposal Network](https://mlsanity.com/paper/1707.08101/) (2017)
- [COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing](https://mlsanity.com/paper/2011.11270/) (2020)
- [UNO Push: Unified Nonprehensile Object Pushing via Non-Parametric Estimation and Model Predictive Control](https://mlsanity.com/paper/2403.13274/) (2024)
- [Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation](https://mlsanity.com/paper/2007.13421/) (2020)
- [Learning Goal-Oriented Non-Prehensile Pushing in Cluttered Scenes](https://mlsanity.com/paper/2203.02389/) (2022)

## Built on by (most cited first, 5 of 5)

- [Can Context Bridge the Reality Gap? Sim-to-Real Transfer of Context-Aware Policies](https://mlsanity.com/paper/2511.04249/) (2025)
- [SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks](https://mlsanity.com/paper/2509.13949/) (2025)
- [End-to-End Low-Level Neural Control of an Industrial-Grade 6D Magnetic Levitation System](https://mlsanity.com/paper/2509.01388/) (2025)
- [Latent Representations for Visual Proprioception in Inexpensive Robots](https://mlsanity.com/paper/2504.14634/) (2025)
- [MagBotSim: Physics-Based Simulation and Reinforcement Learning Environments for Magnetic Robotics](https://mlsanity.com/paper/2511.16158/) (2025)

## 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.