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Robots can acquire complex manipulation skills by learning policies from expert demonstrations, which is often known as vision-based imitation learning.
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Yu, T.; Quillen, D.; He, Z.; Julian, R.; Narayan, A.; Shively, H.; Bellathur, A.; Hausman, K.; Finn, C.; and Levine, S. 2021 · 1910
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Motion planning diffusion: Learning and planning of robot motions with diffusion models
Carvalho, J.; Le, A. T.; Baierl, M.; Koert, D.; and Peters, J. 2023 · 1923
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Robot learning from demonstration
Atkeson, C. G.; and Schaal, S. 1997 · 1997
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A survey of robot learning from demonstration
Argall, B. D.; Chernova, S.; Veloso, M.; and Browning, B. 2009 · 2009
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Denoising diffusion implicit models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2010
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Optimal transport theory for power-efficient deployment of unmanned aerial vehicles
Mozaffari, M.; Saad, W.; Bennis, M.; and Debbah, M. 2016 · 2016
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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Qi, C. R.; Yi, L.; Su, H.; and Guibas, L. J. 2017 · 2017
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Vision-based multi-task manipulation for inexpensive robots using end-to-end learning from demonstration
Rahmatizadeh, R.; Abolghasemi, P.; Bölöni, L.; and Levine, S. 2018 · 2018
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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Rajeswaran, A.; Kumar, V.; Gupta, A.; Vezzani, G.; Schulman, J.; Todorov, E.; and Levine, S. 2018 · 2018
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Deep imitation learning for complex manipulation tasks from virtual reality teleoperation
Zhang, T.; McCarthy, Z.; Jow, O.; Lee, D.; Chen, X.; Goldberg, K.; and Abbeel, P. 2018 · 2018
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Self-supervised correspondence in visuomotor policy learning
Florence, P.; Manuelli, L.; and Tedrake, R. 2019 · 2019
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Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
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Strictly batch imitation learning by energy-based distribution matching
Jarrett, D.; Bica, I.; and van der Schaar, M. 2020 · 2020
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The magical benchmark for robust imitation
Toyer, S.; Shah, R.; Critch, A.; and Russell, S. 2020 · 2020
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Conditional image generation with score-based diffusion models
Batzolis, G.; Stanczuk, J.; Schönlieb, C.-B.; and Etmann, C. 2021 · 2021
Cited alongside, same era.
Il-flow: Imitation learning from observation using normalizing flows
Chang, W.-D.; Higuera, J. C. G.; Fujimoto, S.; Meger, D.; and Dudek, G. 2022 · 2022
Cited alongside, same era.
Implicit behavioral cloning
Florence, P.; Lynch, C.; Zeng, A.; Ramirez, O. A.; Wahid, A.; Downs, L.; Wong, A.; Lee, J.; Mordatch, I.; and Tompson, J. 2022 · 2022
Cited alongside, same era.
Flow matching for generative modeling
Lipman, Y.; Chen, R. T.; Ben-Hamu, H.; Nickel, M.; and Le, M. 2022 · 2022
Cited alongside, same era.
Diffusion policies as an expressive policy class for offline reinforcement learning
Song, Y.; Dhariwal, P.; Chen, M.; and Sutskever, I. 2023 · 2023
Later among the works it cites.
Se (3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion
Urain, J.; Funk, N.; Peters, J.; and Chalvatzaki, G. 2023 · 2023
Later among the works it cites.
Boosting Continuous Control with Consistency Policy
Chen, Y.; Li, H.; and Zhao, D. 2024 · 2024
Closest in time.
Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
Ding, Z.; and Jin, C. 2024 · 2024
Closest in time.
AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies
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Wang, Z.; Hunt, J. J.; and Zhou, M. 2022 · 2022
Cited alongside, same era.
Stablevideo: Text-driven consistency-aware diffusion video editing
Chai, W.; Guo, X.; Wang, G.; and Lu, Y. 2023 · 2023
Cited alongside, same era.
Diffusion policy: Visuomotor policy learning via action diffusion
Chi, C.; Feng, S.; Du, Y.; Xu, Z.; Cousineau, E.; Burchfiel, B.; and Song, S. 2023 · 2023
Cited alongside, same era.
Diffusion self-guidance for controllable image generation
Epstein, D.; Jabri, A.; Poole, B.; Efros, A.; and Holynski, A. 2023 · 2023
Cited alongside, same era.
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
Feng, J.; Lee, J.; Geisler, S.; Gunnemann, S.; and Triebel, R. 2023 · 2023
Cited alongside, same era.
Latent consistency models: Synthesizing high-resolution images with few-step inference
Luo, S.; Tan, Y.; Huang, L.; Li, J.; and Zhao, H. 2023 · 2023
Cited alongside, same era.
Imitating human behaviour with diffusion models
Pearce, T.; Rashid, T.; Kanervisto, A.; Bignell, D.; Sun, M.; Georgescu, R.; Macua, S. V.; Tan, S. Z.; Momennejad, I.; Hofmann, K.; et al. 2023 · 2023
Cited alongside, same era.
Goal-conditioned imitation learning using score-based diffusion policies
Reuss, M.; Li, M.; Jia, X.; and Lioutikov, R. 2023 · 2023
Cited alongside, same era.
Hu, X.; Liu, B.; Liu, X.; and Liu, Q. 2024 · 2024
Closest in time.
Robust Visual Imitation Learning with Inverse Dynamics Representations
Li, S.; Wang, X.; Zuo, R.; Sun, K.; Cui, L.; Ding, J.; Liu, P.; and Ma, Z. 2024 · 2024
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ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation
Lu, G.; Gao, Z.; Chen, T.; Dai, W.; Wang, Z.; and Tang, Y. 2024 · 2024
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Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation
Prasad, A.; Lin, K.; Wu, J.; Zhou, L.; and Bohg, J. 2024 · 2024
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Flow Matching Imitation Learning for Multi-Support Manipulation
Rouxel, Q.; Ferrari, A.; Ivaldi, S.; and Mouret, J.-B. 2024 · 2024
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Safe Offline Reinforcement Learning using Trajectory-Level Diffusion Models
Römer, R.; Brunke, L.; Schuck, M.; and Schoellig, A. P. 2024 · 2024
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Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
Yang, L.; Zhang, Z.; Zhang, Z.; Liu, X.; Xu, M.; Zhang, W.; Meng, C.; Ermon, S.; and Cui, B. 2024 · 2024
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A survey of imitation learning: Algorithms, recent developments, and challenges
Zare, M.; Kebria, P. M.; Khosravi, A.; and Nahavandi, S. 2024 · 2024
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3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
Ze, Y.; Zhang, G.; Zhang, K.; Hu, C.; Wang, M.; and Xu, H. 2024 · 2024
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