Disentangled Relational Representations for Explaining and Learning from Demonstration

31 July 2019

Papers citing "Disentangled Relational Representations for Explaining and Learning from Demonstration"

9 / 9 papers shown

Title
Instruction-driven history-aware policies for robotic manipulations Pierre-Louis Guhur Shizhe Chen Ricardo Garcia Pinel Makarand Tapaswi Ivan Laptev Cordelia Schmid LM&Ro 113 102 0 11 Sep 2022
Learning Perceptual Concepts by Bootstrapping from Human Queries Andreea Bobu Chris Paxton Wei Yang Balakumar Sundaralingam Yu-Wei Chao Maya Cakmak Dieter Fox SSL 35 17 0 09 Nov 2021
StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects Weiyu Liu Chris Paxton Tucker Hermans Dieter Fox 37 92 0 19 Oct 2021
Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition Bo Miao Liguang Zhou Ajmal Mian Tin Lun Lam Yangsheng Xu 14 26 0 01 Aug 2021
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter Hanbo Zhang Deyu Yang Han Wang Binglei Zhao Xuguang Lan Jishiyu Ding Nanning Zheng 44 40 0 29 Apr 2021
Interpretability in Contact-Rich Manipulation via Kinodynamic Images Ioanna Mitsioni Joonatan Mänttäri Y. Karayiannidis John Folkesson Danica Kragic FAtt 11 3 0 23 Feb 2021
Towards Deep Robot Learning with Optimizer applicable to Non-stationary Problems Taisuke Kobayashi ODL 20 9 0 31 Jul 2020
Learning from Demonstration with Weakly Supervised Disentanglement Yordan V. Hristov S. Ramamoorthy DRL 27 9 0 16 Jun 2020
Robust and Interpretable Grounding of Spatial References with Relation Networks Tsung-Yen Yang Andrew S. Lan Karthik Narasimham 29 12 0 02 May 2020