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Many real-world manipulation tasks consist of a series of subtasks that are significantly different from one another.
Achieving several goals simultaneously
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Policy distillation
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Attention is all you need
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Learning to dress: Synthesizing human dressing motion via deep reinforcement learning
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Data-efficient hierarchical reinforcement learning
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Dextreme: Transfer of agile in-hand manipulation from simulation to reality
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Visual dexterity: In-hand dexterous manipulation from depth
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Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation
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OpenAI, I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, J. Schneider, N. Tezak, J. Tworek, P. Welinder, L. Weng, Q. Yuan, W. Zaremba, and L. Zhang · 2019
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Composing complex skills by learning transition policies
Y. Lee, S.-H. Sun, S. Somasundaram, E. S. Hu, and J. J. Lim · 2019
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Mcp: Learning composable hierarchical control with multiplicative compositional policies
X. B. Peng, M. Chang, G. Zhang, P. Abbeel, and S. Levine · 2019
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Compile: Compositional imitation learning and execution
T. Kipf, Y. Li, H. Dai, V. Zambaldi, A. Sanchez-Gonzalez, E. Grefenstette, P. Kohli, and P. Battaglia · 2019
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Regression planning networks
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Densefusion: 6d object pose estimation by iterative dense fusion
C. Wang, D. Xu, Y. Zhu, R. Martín-Martín, C. Lu, L. Fei-Fei, and S. Savarese · 2019
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Learning dexterous in-hand manipulation
O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, et al · 2020
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Y. Chen, T. Wu, S. Wang, X. Feng, J. Jiang, Z. Lu, S. McAleer, H. Dong, S.-C. Zhu, and Y. Yang · 2022
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In-Hand Object Rotation via Rapid Motor Adaptation
H. Qi, A. Kumar, R. Calandra, Y. Ma, and J. Malik · 2022
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Do as i can, not as i say: Grounding language in robotic affordances
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, et al · 2022
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Generalizable task planning through representation pretraining
C. Wang, D. Xu, and L. Fei-Fei · 2022
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Stap: Sequencing task-agnostic policies
C. Agia, T. Migimatsu, J. Wu, and J. Bohg · 2022
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Learning purely tactile in-hand manipulation with a torque-controlled hand
L. Sievers, J. Pitz, and B. Bäuml · 2022
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Densetact: Optical tactile sensor for dense shape reconstruction
W. K. Do and M. Kennedy · 2022
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Viola: Imitation learning for vision-based manipulation with object proposal priors
Y. Zhu, A. Joshi, P. Stone, and Y. Zhu · 2022
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XMem: Long-term video object segmentation with an atkinson-shiffrin memory model
H. K. Cheng and A. G. Schwing · 2022
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Dexpoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation
Y. Qin, B. Huang, Z.-H. Yin, H. Su, and X. Wang · 2023
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Rotating without seeing: Towards in-hand dexterity through touch
Z.-H. Yin, B. Huang, Y. Qin, Q. Chen, and X. Wang · 2023
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Robopianist: A benchmark for high-dimensional robot control
K. Zakka, L. Smith, N. Gileadi, T. Howell, X. B. Peng, S. Singh, Y. Tassa, P. Florence, A. Zeng, and P. Abbeel · 2023
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Dexterity from touch: Self-supervised pre-training of tactile representations with robotic play
I. Guzey, B. Evans, S. Chintala, and L. Pinto · 2023
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Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation
G. Khandate, S. Shang, E. T. Chang, T. L. Saidi, J. Adams, and M. Ciocarlie · 2023
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Mimicplay: Long-horizon imitation learning by watching human play
C. Wang, L. Fan, J. Sun, R. Zhang, L. Fei-Fei, D. Xu, Y. Zhu, and A. Anandkumar · 2023
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Tidybot: Personalized robot assistance with large language models
J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser · 2023
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Latent plans for task-agnostic offline reinforcement learning
E. Rosete-Beas, O. Mees, G. Kalweit, J. Boedecker, and W. Burgard · 2023
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Dextrous tactile in-hand manipulation using a modular reinforcement learning architecture
J. Pitz, L. Röstel, L. Sievers, and B. Bäuml · 2023
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