mlsanityData to 14 Oct 2026

A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives

arXiv 2111.01510 ↗First posted 2 Nov 2021cs.RO cs.AIcited by 6

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Abstract (arXiv)

Many possible fields of application of robots in real world settings hinge on the ability of robots to grasp objects. As a result, robot grasping has been an active field of research for many years. With our publication we contribute to the endeavor of enabling robots to grasp, with a particular focus on bin picking applications. Bin picking is especially challenging due to the often cluttered and unstructured arrangement of objects and the often limited graspability of objects by simple top down grasps. To tackle these challenges, we propose a fully self-supervised reinforcement learning approach based on a hybrid discrete-continuous adaptation of soft actor-critic (SAC). We employ parametrized motion primitives for pushing and grasping movements in order to enable a flexibly adaptable behavior to the difficult setups we consider. Furthermore, we use data augmentation to increase sample efficiency. We demonnstrate our proposed method on challenging picking scenarios in which planar grasp learning or action discretization methods would face a lot of difficulties

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 26 of its 40 references that are in the map. First posted on arXiv 2 Nov 2021.

Builds on

26 of 40 references.

References · 26

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  4. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  5. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  6. 2020Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixelscited by 585
  7. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 316
  8. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 289
  9. 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 264
  10. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 242
  11. 2021Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenescited by 218
  12. 2015Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hourscited by 209
14 more
  1. 2018Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learningcited by 128
  2. 2013Deep Learning for Detecting Robotic Graspscited by 116
  3. 2019Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrationscited by 107
  4. 2013Data-Driven Grasp Synthesis - A Surveycited by 106
  5. 2014Real-Time Grasp Detection Using Convolutional Neural Networkscited by 76
  6. 2019Antipodal Robotic Grasping using Generative Residual Convolutional Neural Networkcited by 59
  7. 2016Robotic Grasp Detection using Deep Convolutional Neural Networkscited by 47
  8. 2020Continuous-Discrete Reinforcement Learning for Hybrid Control in Roboticscited by 44
  9. 2019Discrete and Continuous Action Representation for Practical RL in Video Gamescited by 39
  10. 2016Learning to Push by Grasping: Using multiple tasks for effective learningcited by 37
  11. 2019Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Cluttercited by 30
  12. 2019Robot Learning of Shifting Objects for Grasping in Cluttered Environmentscited by 13
  13. 2020Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Datacited by 13
  14. 2021Robot Learning of 6 DoF Grasping using Model-based Adaptive Primitivescited by 7

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Cited by year

Citing papers per year: 2023: 3; 2024: 3.

6 papers.

  1. 2023HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulationcited by 29
  2. 2023Uncertainty-driven Exploration Strategies for Online Grasp Learningcited by 6
  3. 2024HACMan++: Spatially-Grounded Motion Primitives for Manipulationcited by 6
  4. 2024Enhancing Exploration with Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulationcited by 3
  5. 2023DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenescited by 1
  6. 2024Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Pickingcited by 1