Fetching the paper…
Reading the bibliography…
When searching for objects in cluttered environments, it is often necessary to perform complex interactions in order to move occluding objects out of the way and fully reveal the object of interest and make it graspable.
“Grasp synthesis in cluttered environments for dexterous hands”
Dmitry Berenson and Siddhartha Srinivasa · 2008
Earlier work this paper cites.
“Interactive segmentation for manipulation in unstructured environments”
Jacqueline Kenney, Thomas Buckley and Oliver Brock · 2009
Earlier work this paper cites.
“Guided pushing for object singulation”
Tucker Hermans, James Rehg and Aaron Bobick · 2012
Earlier work this paper cites.
“Maximally informative interaction learning for scene exploration”
Herke Van, Oliver Kroemer, Heni Amor and Jan Peters · 2012
Earlier work this paper cites.
“Perceiving, learning, and exploiting object affordances for autonomous pile manipulation”
Dov Katz et al · 2014
Earlier work this paper cites.
“Using manipulation primitives for object sorting in cluttered environments”
Megha Gupta, Jörg Müller and Gaurav Sukhatme · 2014
Earlier work this paper cites.
“Combined task and motion planning through an extensible planner-independent interface layer”
Siddharth Srivastava et al · 2014
Earlier work this paper cites.
“Continuous control with deep reinforcement learning”
Timothy Lillicrap et al · 2015
Earlier work this paper cites.
“Benchmarking in manipulation research: The YCB object and model set and benchmarking protocols”
Berk Calli et al · 2015
Earlier work this paper cites.
“An integrated approach to visual perception of articulated objects”
Roberto Martín-Martín, Sebastian Höfer and Oliver Brock · 2016
Earlier work this paper cites.
“Asymmetric actor critic for image-based robot learning”
Lerrel Pinto et al · 2017
Earlier work this paper cites.
“Interactive perception: Leveraging action in perception and perception in action”
Jeannette Bohg et al · 2017
Earlier work this paper cites.
“Randomized physics-based motion planning for grasping in cluttered and uncertain environments”
Mark Moll, Lydia Kavraki and Jan Rosell · 2017
Earlier work this paper cites.
“Learning deep policies for robot bin picking by simulating robust grasping sequences”
Jeffrey Mahler and Ken Goldberg · 2017
Cited alongside, same era.
“End-to-end learning of semantic grasping”
Eric Jang et al · 2017
Cited alongside, same era.
“pybullet, a Python module for physics simulation, games, robotics and machine learning”,
Erwin Coumans and Yunfei Bai · 2017
Cited alongside, same era.
“Bayesian policy gradients via alpha divergence dropout inference”
Peter Henderson, Thang Doan, Riashat Islam and David Meger · 2017
Cited alongside, same era.
“Learning synergies between pushing and grasping with self-supervised deep reinforcement learning”
Andy Zeng et al · 2018
Cited alongside, same era.
“Online Planning for Target Object Search in Clutter under Partial Observability”
Yuchen Xiao et al · 2019
Later among the works it cites.
“Object Finding in Cluttered Scenes Using Interactive Perception”
Tonci Novkovic et al · 2019
Later among the works it cites.
“Robot learning of shifting objects for grasping in cluttered environments”
Lars Berscheid, Pascal Meißner and Torsten Kröger · 2019
Later among the works it cites.
“Scaling Robot Supervision to Hundreds of Hours with RoboTurk: Robotic Manipulation Dataset through Human Reasoning and Dexterity”
A. Mandlekar et al · 2019
Later among the works it cites.
“Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks”
Michelle Lee et al · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dmitry Kalashnikov et al · 2018
Cited alongside, same era.
“Gibson Env: real-world perception for embodied agents”
Fei Xia et al · 2018
Cited alongside, same era.
“Sim-to-real: Learning agile locomotion for quadruped robots”
Jie Tan et al · 2018
Cited alongside, same era.
“An intriguing failing of convolutional neural networks and the coordconv solution”
Rosanne Liu et al · 2018
Cited alongside, same era.
Alexander Sax et al · 2018
Cited alongside, same era.
“Reinforcement learning: An introduction”
Richard Sutton and Andrew Barto · 2018
Cited alongside, same era.
“Mechanical search: Multi-step retrieval of a target object occluded by clutter”
Michael Danielczuk et al · 2019
Cited alongside, same era.
Antonin Raffin et al · 2019
Later among the works it cites.
“Densefusion: 6d object pose estimation by iterative dense fusion”
Chen Wang et al · 2019
Later among the works it cites.
“Gibson Env V2: Embodied Simulation Environments for Interactive Navigation”, 2019
Fei Xia et al · 2019
Later among the works it cites.
“On-policy dataset synthesis for learning robot grasping policies using fully convolutional deep networks”
Vishal Satish, Jeffrey Mahler and Ken Goldberg · 2019
Later among the works it cites.
“A Deep Learning Approach to Grasping the Invisible”
Yang Yang, Hengyue Liang and Changhyun Choi · 2020
Closest in time.
Taewon Kim, Yeseong Park, Youngbin Park and Il Suh · 2020
Closest in time.
“Learning to singulate objects using a push proposal network”
Andreas Eitel, Nico Hauff and Wolfram Burgard · 2020
Closest in time.