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Building a robot that can understand and learn to interact by watching humans has inspired several vision problems.
Smoothing and differentiation of data by simplified least squares procedures
Abraham Savitzky and Marcel JE Golay · 1964
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The ecological approach to visual perception
JJ Gibson · 1979
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Rapidly-exploring random trees: Progress and prospects
S. M. Lavalle and J. J. Kuffner · 2000
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Planning algorithms
Steven M LaValle · 2006
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On learning, representing, and generalizing a task in a humanoid robot
Sylvain Calinon, Florent Guenter, and Aude Billard · 2007
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A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
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From 3d scene geometry to human workspace
Abhinav Gupta, Scott Satkin, Alexei A Efros, and Martial Hebert · 2011
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Sampling-based algorithms for optimal motion planning
Sertac Karaman and Emilio Frazzoli · 2011
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People watching: Human actions as a cue for single view geometry
David F Fouhey, Vincent Delaitre, Abhinav Gupta, Alexei A Efros, Ivan Laptev, and Josef Sivic · 2012
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Dynamic movement primitives for human-robot interaction: Comparison with human behavioral observation
M. Prada, A. Remazeilles, A. Koene, and S. Endo · 2013
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Scene parsing by integrating function, geometry and appearance models
Yibiao Zhao and Song-Chun Zhu · 2013
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D Eigen and R Fergus · 2014
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Action-reaction: Forecasting the dynamics of human interaction
De-An Huang and Kris M Kitani · 2014
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A hierarchical representation for future action prediction
Tian Lan, Tsung-Chuan Chen, and Silvio Savarese · 2014
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Affordance of object parts from geometric features
Austin Myers, Angjoo Kanazawa, Cornelia Fermuller, and Yiannis Aloimonos · 2014
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Reasoning about object affordances in a knowledge base representation
Yuke Zhu, Alireza Fathi, and Li Fei-Fei · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Anticipating human activities using object affordances for reactive robotic response
Hema S Koppula and Ashutosh Saxena · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Interactive hierarchical task learning from a single demonstration
Anahita Mohseni-Kabir, Charles Rich, Sonia Chernova, Candace L Sidner, and Daniel Miller · 2015
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Affordance detection of tool parts from geometric features
Austin Myers, Ching L Teo, Cornelia Fermüller, and Yiannis Aloimonos · 2015
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Marr revisited: 2d-3d alignment via surface normal prediction
Aayush Bansal, Bryan Russell, and Abhinav Gupta · 2016
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Unifying count-based exploration and intrinsic motivation
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, and Remi Munos · 2016
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First person action-object detection with egonet
Gedas Bertasius, Hyun Soo Park, Stella X Yu, and Jianbo Shi · 2016
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Learning object affordances by leveraging the combination of human-guidance and self-exploration
Vivian Chu, Tesca Fitzgerald, and Andrea L Thomaz · 2016
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Recurrent neural networks for driver activity anticipation via sensory-fusion architecture
Ashesh Jain, Avi Singh, Hema S Koppula, Shane Soh, and Ashutosh Saxena · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
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Learning action maps of large environments via first-person vision
Nicholas Rhinehart and Kris M Kitani · 2016
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A multi-scale cnn for affordance segmentation in rgb images
Anirban Roy and Sinisa Todorovic · 2016
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Predicting motivations of actions by leveraging text
Carl Vondrick, Deniz Oktay, Hamed Pirsiavash, and Antonio Torralba · 2016
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Inferring forces and learning human utilities from videos
Yixin Zhu, Chenfanfu Jiang, Yibiao Zhao, Demetri Terzopoulos, and Song-Chun Zhu · 2016
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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Learning cooperative visual dialog agents with deep reinforcement learning
Abhishek Das, Satwik Kottur, José M.F. Moura, Stefan Lee, and Dhruv Batra · 2017
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Learning generalizable surface cleaning actions from demonstration
Sarah Elliott, Zhe Xu, and Maya Cakmak · 2017
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Next-active-object prediction from egocentric videos
Antonino Furnari, Sebastiano Battiato, Kristen Grauman, and Giovanni Maria Farinella · 2017
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Red: Reinforced encoder-decoder networks for action anticipation
Jiyang Gao, Zhenheng Yang, and Ram Nevatia · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks
Guilherme J Maeda, Gerhard Neumann, Marco Ewerton, Rudolf Lioutikov, Oliver Kroemer, and Jan Peters · 2017
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Combining self-supervised learning and imitation for vision-based rope manipulation
Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell · 2017
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Weakly supervised affordance detection
Johann Sawatzky, Abhilash Srikantha, and Juergen Gall · 2017
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# exploration: A study of count-based exploration for deep reinforcement learning
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, OpenAI Xi Chen, Yan Duan, John Schulman, Filip DeTurck, and Pieter Abbeel · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Decomposing motion and content for natural video sequence prediction
Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, and Honglak Lee · 2017
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Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi · 2017
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When will you do what?-anticipating temporal occurrences of activities
Yazan Abu Farha, Alexander Richard, and Juergen Gall · 2018
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Differentiable mpc for end-to-end planning and control
Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, and J. Zico Kolter · 2018
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Applied optimal control: optimization, estimation, and control
Arthur E Bryson and Yu-Chi Ho · 2018
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Scaling egocentric vision: The epic-kitchens dataset
Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray · 2018
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Scaling egocentric vision: The epic-kitchens dataset
Anticipative video transformer
Rohit Girdhar and Kristen Grauman · 2021
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Gridtopix: Training embodied agents with minimal supervision
Unnat Jain, Iou-Jen Liu, Svetlana Lazebnik, Aniruddha Kembhavi, Luca Weihs, and Alexander G Schwing · 2021
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A workflow for offline model-free robotic reinforcement learning
Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, and Sergey Levine · 2021
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Polymetis
Yixin Lin, Austin S. Wang, Giovanni Sutanto, Akshara Rai, and Franziska Meier · 2021
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Cooperative exploration for multi-agent deep reinforcement learning
Iou-Jen Liu, Unnat Jain, Raymond A Yeh, and Alexander Schwing · 2021
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What matters in learning from offline human demonstrations for robot manipulation
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Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray · 2018
Cited alongside, same era.
Incremental task modification via corrective demonstrations
Reymundo A Gutierrez, Vivian Chu, Andrea L Thomaz, and Scott Niekum · 2018
Cited alongside, same era.
Visual affordance and function understanding: a survey. arxiv
M Hassanin, S Khan, and M Tahtali · 2018
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Two can play this game: Visual dialog with discriminative question generation and answering
Unnat Jain, Svetlana Lazebnik, and Alexander G Schwing · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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In the eye of beholder: Joint learning of gaze and actions in first person video
Yin Li, Miao Liu, and James M Rehg · 2018
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
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The surprising effectiveness of representation learning for visual imitation
Jyothish Pari, Nur Muhammad, Sridhar Pandian Arunachalam, Lerrel Pinto, et al · 2021
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Interesting object, curious agent: Learning task-agnostic exploration
Simone Parisi, Victoria Dean, Deepak Pathak, and Abhinav Gupta · 2021
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Offline reinforcement learning from images with latent space models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, and Chelsea Finn · 2021
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An exploration of embodied visual exploration
Santhosh K Ramakrishnan, Dinesh Jayaraman, and Kristen Grauman · 2021
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Frankmocap: A monocular 3d whole-body pose estimation system via regression and integration
Yu Rong, Takaaki Shiratori, and Hanbyul Joo · 2021
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Rrl: Resnet as representation for reinforcement learning
Rutav M Shah and Vikash Kumar · 2021
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Concept2robot: Learning manipulation concepts from instructions and human demonstrations
Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, and Jeannette Bohg · 2021
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d3rlpy: An offline deep reinforcement library
Michita Imai Takuma Seno · 2021
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Pushing it out of the way: Interactive visual navigation
Kuo-Hao Zeng, Luca Weihs, Ali Farhadi, and Roozbeh Mottaghi · 2021
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Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation
Sridhar Pandian Arunachalam, Sneha Silwal, Ben Evans, and Lerrel Pinto · 2022
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Human-to-robot imitation in the wild
Shikhar Bahl, Abhinav Gupta, and Deepak Pathak · 2022
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Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100
Dima Damen, Hazel Doughty, Giovanni Maria Farinella, , Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray · 2022
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Epic-kitchens visor benchmark: Video segmentations and object relations
Ahmad Darkhalil, Dandan Shan, Bin Zhu, Jian Ma, Amlan Kar, Richard Higgins, Sanja Fidler, David Fouhey, and Dima Damen · 2022
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Ifor: Iterative flow minimization for robotic object rearrangement
Ankit Goyal, Arsalan Mousavian, Chris Paxton, Yu-Wei Chao, Brian Okorn, Jia Deng, and Dieter Fox · 2022
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Human hands as probes for interactive object understanding
Mohit Goyal, Sahil Modi, Rishabh Goyal, and Saurabh Gupta · 2022
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Ego4d: Around the world in 3,000 hours of egocentric video
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, et al · 2022
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Joint hand motion and interaction hotspots prediction from egocentric videos
Shaowei Liu, Subarna Tripathi, Somdeb Majumdar, and Xiaolong Wang · 2022
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Hoi4d: A 4d egocentric dataset for category-level human-object interaction
Yunze Liu, Yun Liu, Che Jiang, Kangbo Lyu, Weikang Wan, Hao Shen, Boqiang Liang, Zhoujie Fu, He Wang, and Li Yi · 2022
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Physical interaction as communication: Learning robot objectives online from human corrections
Dylan P Losey, Andrea Bajcsy, Marcia K O’Malley, and Anca D Dragan · 2022
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Vip: Towards universal visual reward and representation via value-implicit pre-training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, and Amy Zhang · 2022
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Intention-conditioned long-term human egocentric action forecasting@ ego4d challenge 2022
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Memory-augmented reinforcement learning for image-goal navigation
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Learning state-aware visual representations from audible interactions
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R3m: A universal visual representation for robot manipulation
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Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
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The unsurprising effectiveness of pre-trained vision models for control
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Dexmv: Imitation learning for dexterous manipulation from human videos
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Real-world robot learning with masked visual pre-training
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Videodex: Learning dexterity from internet videos
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Cliport: What and where pathways for robotic manipulation
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Masked visual pre-training for motor control
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Offline visual representation learning for embodied navigation
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Learning continuous grasping function with a dexterous hand from human demonstrations
Jianglong Ye, Jiashun Wang, Binghao Huang, Yuzhe Qin, and Xiaolong Wang · 2022
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What’s in your hands? 3d reconstruction of generic objects in hands
Yufei Ye, Abhinav Gupta, and Shubham Tulsiani · 2022
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Detecting twenty-thousand classes using image-level supervision
Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, and Ishan Misra · 2022
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Alan: Autonomously exploring robotic agents in the real world
Russell Mendonca, Shikhar Bahl, and Deepak Pathak · 2023
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