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Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and dishwashers while assisting humans in performing day-to-day tasks.
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Scott Niekum, Sarah Osentoski, Christopher Atkeson and Andrew Barto · 2015
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Karol Hausman, Scott Niekum, Sarah Osentoski and Gaurav Sukhatme · 2015
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Yizhou Liu et al · 2019
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“Coupled recursive estimation for online interactive perception of articulated objects”
Roberto Martín-Martín and Oliver Brock · 2019
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Ben Abbatematteo, Stefanie Tellex and George Konidaris · 2019
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Karthik Desingh, Shiyang Lu, Anthony Opipari and Odest Jenkins · 2019
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“Interactive perception: Leveraging action in perception and perception in action”
Jeannette Bohg et al · 2017
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“C-learn: Learning geometric constraints from demonstrations for multi-step manipulation in shared autonomy”
Claudia Pérez-D’Arpino and Julie Shah · 2017
Cited alongside, same era.
“Efficient hierarchical robot motion planning under uncertainty and hybrid dynamics”
Ajinkya Jain and Scott Niekum · 2018
Cited alongside, same era.
“Deep part induction from articulated object pairs”
Li Yi et al · 2018
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“Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning”
Abhishek Gupta et al · 2019
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“A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms”
Oliver Kroemer, Scott Niekum and George Konidaris · 2019
Cited alongside, same era.
Yan-bin Jia · 2019
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“Learning hybrid object kinematics for efficient hierarchical planning under uncertainty”
Ajinkya Jain and Scott Niekum · 2020
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“Category-Level Articulated Object Pose Estimation”
Xiaolong Li et al · 2020
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“SAPIEN: A SimulAted Part-based Interactive ENvironment”
Fanbo Xiang et al · 2020
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“YOLOv4: Optimal Speed and Accuracy of Object Detection”
Alexey Bochkovskiy, Chien-Yao Wang and Hong-Yuan Liao · 2020
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“A multiview approach to learning articulated motion models”
Andrea Daniele, Thomas Howard and Matthew Walter · 2020
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