Fetching the paper…
Reading the bibliography…
Dexterous manipulation is a crucial yet highly complex challenge in humanoid robotics, demanding precise, adaptable, and sample-efficient learning methods.
Solving rubik’s cube with a robot hand
Akkaya, I., Andrychowicz, M., Chociej, M., Litwin, M., McGrew, B., Petron, A., Paino, A., Plappert, M., Powell, G., Ribas, R., Schneider, J., Tezak, N., Tworek, J., Welinder, P., Weng, L., Yuan, Q., Zaremba, W., and Zhang, L. (2019) · 1910
Earlier work this paper cites.
Contextual reinforcement learning of visuo-tactile multi-fingered grasping policies
Kumar, V., Hermans, T., Fox, D., Birchfield, S., and Tremblay, J. (2019) · 1911
Earlier work this paper cites.
Imitation learning
Mikami, A. (2009) · 1918
Earlier work this paper cites.
An overview of dexterous manipulation
Okamura, A., Smaby, N., and Cutkosky, M. (2000) · 2000
Earlier work this paper cites.
A 25 degrees of freedom hand geometrical model for better hand attitude simulation
Savescu, A.-V., Cheze, L., Wang, X., Beurier, G., and Verriest, J.-P. (2004) · 2004
Earlier work this paper cites.
Awac: Accelerating online reinforcement learning with offline datasets
Nair, A., Gupta, A., Dalal, M., and Levine, S. (2020) · 2006
Earlier work this paper cites.
Underactuated Robotic Hands
Birglen, L., Laliberté, T., and Gosselin, C. (2008) · 2008
Earlier work this paper cites.
Tamer: Training an agent manually via evaluative reinforcement
Bradley Knox, W. and Stone, P. (2008) · 2008
Earlier work this paper cites.
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., and Friedman, J. (2009) · 2009
Earlier work this paper cites.
Tactile sensing—from humans to humanoids
Dahiya, R., Metta, G., Valle, M., and Sandini, G. (2010) · 2010
Earlier work this paper cites.
Tactile guidance for policy adaptation
Argall, B. D., Sauser, E. L., and Billard, A. G. (2011) · 2011
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, D. (2011) · 2011
Earlier work this paper cites.
Learning to grasp with parental scaffolding
Ugur, E., Celikkanat, H., Sahin, E., Nagai, Y., and Oztop, E. (2011) · 2011
Earlier work this paper cites.
Human-in-the-loop imitation learning using remote teleoperation
Mandlekar, A., Xu, D., Martín-Martín, R., Zhu, Y., Fei-Fei, L., and Savarese, S. (2020) · 2012
Earlier work this paper cites.
Iterative learning of grasp adaptation through human corrections
Sauser, E. L., Argall, B. D., Metta, G., and Billard, A. G. (2012) · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y. (2012) · 2012
Earlier work this paper cites.
Learning robot in-hand manipulation with tactile features
van Hoof, H., Hermans, T., Neumann, G., and Peters, J. (2015) · 2015
Earlier work this paper cites.
Incremental imitation learning of context-dependent motor skills
Ewerton, M., Maeda, G., Kollegger, G., Wiemeyer, J., and Peters, J. (2016) · 2016
Earlier work this paper cites.
Adaptation and coaching of periodic motion primitives through physical and visual interaction
Gams, A., Petrič, T., Do, M., Nemec, B., Morimoto, J., Asfour, T., and Ude, A. (2016) · 2016
Earlier work this paper cites.
Learning dexterous manipulation for a soft robotic hand from human demonstrations
Gupta, A., Eppner, C., Levine, S., and Abbeel, P. (2016) · 2016
Earlier work this paper cites.
Robot hands
Melchiorri, C. and Kaneko, M. (2016) · 2016
Earlier work this paper cites.
Artificial intelligence: a modern approach
Russell, S. J. and Norvig, P. (2016) · 2016
Earlier work this paper cites.
Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands
Santello, M., Bianchi, M., Gabiccini, M., Ricciardi, E., Salvietti, G., Prattichizzo, D., Ernst, M., Moscatelli, A., Jörntell, H., Kappers, A. M., Kyriakopoulos, K., Albu-Schäffer, A., Castellini, C., and Bicchi, A. (2016) · 2016
Earlier work this paper cites.
Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T. B., Martic, M., Legg, S., and Amodei, D. (2017) · 2017
Earlier work this paper cites.
Interactive learning from policy-dependent human feedback
Macglashan, J., Ho, M. K., Loftin, R., Peng, B., Wang, G., Roberts, D. L., Taylor, M. E., and Littman, M. L. (2017) · 2017
Earlier work this paper cites.
Transferring skills to humanoid robots by extracting semantic representations from observations of human activities
Ramirez-Amaro, K., Beetz, M., and Cheng, G. (2017) · 2017
Earlier work this paper cites.
A topology of shared control systems—finding common ground in diversity
Abbink, D. A., Carlson, T., Mulder, M., de Winter, J. C. F., Aminravan, F., Gibo, T. L., and Boer, E. R. (2018) · 2018
Earlier work this paper cites.
On policy learning robust to irreversible events: An application to robotic in-hand manipulation
Falco, P., Attawia, A., Saveriano, M., and Lee, D. (2018) · 2018
Earlier work this paper cites.
Soft actor-critic algorithms and applications
Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., and Levine, S. (2018) · 2018
Earlier work this paper cites.
Deep q-learning from demonstrations
Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Osband, I., Dulac-Arnold, G., Agapiou, J., Leibo, J., and Gruslys, A. (2018) · 2018
Earlier work this paper cites.
Effective robot skill synthesis via divided control
Kaya, O. and Oztop, E. (2018) · 2018
Earlier work this paper cites.
Learning forward and inverse kinematics maps efficiently
Kubus, D., Rayyes, R., and Steil, J. J. (2018) · 2018
Earlier work this paper cites.
Overcoming exploration in reinforcement learning with demonstrations
Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., and Abbeel, P. (2018) · 2018
Earlier work this paper cites.
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., and Peters, J. (2018) · 2018
Earlier work this paper cites.
Interactive learning with corrective feedback for policies based on deep neural networks
Pérez-Dattari, R., Celemin, C., Ruiz-del-Solar, J., and Kober, J. (2020) · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G. (2018) · 2018
Earlier work this paper cites.
Reinforcement learning of motor skills using policy search and human corrective advice
Celemin, C., Maeda, G., Ruiz-del-Solar, J., Peters, J., and Kober, J. (2019) · 2019
Earlier work this paper cites.
An interactive framework for learning continuous actions policies based on corrective feedback
Celemin, C. and Ruiz-del-Solar, J. (2019) · 2019
Earlier work this paper cites.
Hg-dagger: Interactive imitation learning with human experts
Kelly, M., Sidrane, C., Driggs-Campbell, K., and Kochenderfer, M. J. (2019) · 2019
Earlier work this paper cites.
Tactile sensing and deep reinforcement learning for in-hand manipulation tasks
Melnik, A., Lach, L., Plappert, M., Korthals, T., Haschke, R., and Ritter, H. (2019) · 2019
Earlier work this paper cites.
Investigation into reducing anthropomorphic hand degrees of freedom while maintaining human hand grasping functions
Zarzoura, M., del Moral, P., Awad, M. I., and Tolbah, F. A. (2019) · 2019
Earlier work this paper cites.
Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost
Zhu, H., Gupta, A., Rajeswaran, A., Levine, S., and Kumar, V. (2019) · 2019
Earlier work this paper cites.
Learning dexterous in-hand manipulation
Andrychowicz, O. A. M., Baker, B., Chociej, M., Józefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., Schneider, J., Sidor, S., Tobin, J., Welinder, P., Weng, L., and Zaremba, W. (2020) · 2020
Earlier work this paper cites.
Bootstrap your own latent a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Avila Pires, B., Daniel Guo, Z., Gheshlaghi Azar, M., Piot, B., Kavukcuoglu, K., Munos, R., and Valko, M. (2020) · 2020
Earlier work this paper cites.
Learning object manipulation with dexterous hand-arm systems from human demonstration
Ruppel, P. and Zhang, J. (2020) · 2020
Earlier work this paper cites.
Learning from interventions: Human-robot interaction as both explicit and implicit feedback
Spencer, J., Choudhury, S., Barnes, M., Schmittle, M., Chiang, M., Ramadge, P., and Srinivasa, S. (2020) · 2020
Earlier work this paper cites.
Automated generation of robotic planning domains from observations
Diehl, M., Paxton, C., and Ramirez-Amaro, K. (2021) · 2021
Cited alongside, same era.
Reset-free reinforcement learning via multi-task learning: Learning dexterous manipulation behaviors without human intervention
Gupta, A., Yu, J., Zhao, T. Z., Kumar, V., Rovinsky, A., Xu, K., Devlin, T., and Levine, S. (2021) · 2021
Cited alongside, same era.
Lazydagger: Reducing context switching in interactive imitation learning
Hoque, R., Balakrishna, A., Putterman, C., Luo, M., Brown, D. S., Seita, D., Thananjeyan, B., Novoseller, E., and Goldberg, K. (2021) · 2021
Cited alongside, same era.
Land: Learning to navigate from disengagements
Kahn, G., Abbeel, P., and Levine, S. (2021) · 2021
Cited alongside, same era.
Robust multi-modal policies for industrial assembly via reinforcement learning and demonstrations: A large-scale study
Luo, J., Sushkov, O., Pevceviciute, R., Lian, W., Su, C., Vecerik, M., Ye, N., Schaal, S., and Scholz, J. (2021) · 2021
Cited alongside, same era.
Viola: Imitation learning for vision-based manipulation with object proposal priors
Zhu, Y., Joshi, A., Stone, P., and Zhu, Y. (2023) · 2023
Later among the works it cites.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Zitkovich, B., Yu, T., Xu, S., Xu, P., Xiao, T., Xia, F., Wu, J., Wohlhart, P., Welker, S., Wahid, A., Vuong, Q., Vanhoucke, V., Tran, H., Soricut, R., Singh, A., Singh, J., Sermanet, P., Sanketi, P. R., Salazar, G., Ryoo, M. S., Reymann, K., Rao, K., Pertsch, K., Mordatch, I., Michalewski, H., Lu, Y., Levine, S., Lee, L., Lee, T.-W. E., Leal, I., Kuang, Y., Kalashnikov, D., Julian, R., Joshi, N. J., Irpan, A., Ichter, B., Hsu, J., Herzog, A., Hausman, K., Gopalakrishnan, K., Fu, C., Florence, P., Finn, C., Dubey, K. A., Driess, D., Ding, T., Choromanski, K. M., Chen, X., Chebotar, Y., Carbajal, J., Brown, N., Brohan, A., Arenas, M. G., and Han, K. (2023) · 2023
Later among the works it cites.
Agile hand | agile robots se
AGILE ROBOTS (2023) · 2024
Later among the works it cites.
Successful test of humanoid robots at bmw group plant spartanburg
BMW AG (2024) · 2024
Later among the works it cites.
Visual imitation learning of task-oriented object grasping and rearrangement
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Reinforcement learning with human advice: A survey
Najar, A. and Chetouani, M. (2021) · 2021
Cited alongside, same era.
Inverse reinforcement learning for dexterous hand manipulation
Orbik, J., Agostini, A., and Lee, D. (2021) · 2021
Cited alongside, same era.
State-only imitation learning for dexterous manipulation
Radosavovic, I., Wang, X., Pinto, L., and Malik, J. (2021) · 2021
Cited alongside, same era.
Efficient and Stable Online Learning for Developmental Robots
Rayyes, R. (2021) · 2021
Cited alongside, same era.
Interactive imitation learning in robotics: A survey
Celemin, C., Pérez-Dattari, R., Chisari, E., Franzese, G., de Souza Rosa, L., Prakash, R., Ajanović, Z., Ferraz, M., Valada, A., and Kober, J. (2022) · 2022
Cited alongside, same era.
Correct me if i am wrong: Interactive learning for robotic manipulation
Chisari, E., Welschehold, T., Boedecker, J., Burgard, W., and Valada, A. (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.
Cai, Y., Gao, J., Pohl, C., and Asfour, T. (2024) · 2024
Later among the works it cites.
Springgrasp: Synthesizing compliant, dexterous grasps under shape uncertainty
Chen, S., Bohg, J., and Liu, K. (2024) · 2024
Later among the works it cites.
Task-informed grasping of partially observed objects
de Farias, C., Tamadazte, B., Adjigble, M., Stolkin, R., and Marturi, N. (2024) · 2024
Later among the works it cites.
Ffhflow: A flow-based variational approach for multi-fingered grasp synthesis in real time
Feng, Q., Feng, J., Chen, Z., Triebel, R., and Knoll, A. (2024) · 2024
Later among the works it cites.
Bi-kvil: Keypoints-based visual imitation learning of bimanual manipulation tasks
Gao, J., Jin, X., Krebs, F., Jaquier, N., and Asfour, T. (2024) · 2024
Later among the works it cites.
Metagraspnetv2: All-in-one dataset enabling fast and reliable robotic bin picking via object relationship reasoning and dexterous grasping
Gilles, M., Chen, Y., Zeng, E. Z., Wu, Y., Furmans, K., Wong, A., and Rayyes, R. (2024) · 2024
Later among the works it cites.
Opengrasp-lite version 1.0: A tactile artificial hand with a compliant linkage mechanism
Groß, S., Ratzel, M., Welte, E., Hidalgo-Carvajal, D., Chen, L., Fortunić, E. P., Ganguly, A., Swikir, A., and Haddadin, S. (2024) · 2024
Later among the works it cites.
Learning prehensile dexterity by imitating and emulating state-only observations
Han, Y., Chen, Z., Williams, K. A., and Ravichandar, H. (2024) · 2024
Later among the works it cites.
The dexterous hands rh56dfx series
INSPIRE-ROBOTS (2024) · 2024
Later among the works it cites.
Continual policy distillation of reinforcement learning-based controllers for soft robotic in-hand manipulation
Li, L., Donato, E., Lomonaco, V., and Falotico, E. (2024) · 2024
Later among the works it cites.
Leveraging pretrained latent representations for few-shot imitation learning on an anthropomorphic robotic hand
Liconti, D., Toshimitsu, Y., and Katzschmann, R. (2024) · 2024
Later among the works it cites.
Robot learning on the job: Human-in-the-loop autonomy and learning during deployment
Liu, H., Nasiriany, S., Zhang, L., Bao, Z., and Zhu, Y. (2024) · 2024
Later among the works it cites.
Overcoming the curse of dimensionality in reinforcement learning through approximate factorization
Lu, C., Shi, L., Chen, Z., Wu, C., and Wierman, A. (2024) · 2024
Later among the works it cites.
Dexskills: Skill segmentation using haptic data for learning autonomous long-horizon robotic manipulation tasks
Mao, X., Giudici, G., Coppola, C., Althoefer, K., Farkhatdinov, I., Li, Z., and Jamone, L. (2024) · 2024
Later among the works it cites.
Ih2 azzurra - prensilia - grasping innovation
PRENSILIA (2023) · 2024
Later among the works it cites.
qb softhand2 research - qbrobotics
qbrobotics (2022) · 2024
Later among the works it cites.
Multimodal diffusion transformer: Learning versatile behavior from multimodal goals
Reuss, M., Yağmurlu, Ö., Wenzel, F., and Lioutikov, R. (2024) · 2024
Later among the works it cites.
Tesla to have humanoid robots for internal use next year, musk says
Reuters (2024) · 2024
Later among the works it cites.
Artus lite
Sarcomere Dynamics (2024) · 2024
Later among the works it cites.
Svh 5-finger servo-electric gripping hand
SCHUNK (2023) · 2024
Later among the works it cites.
Rh8d adult size dexterous robot hand — seed robotics
seed robotics (2021) · 2024
Later among the works it cites.
Shadow dexterous hand series - research and development tool
Shadow Robot Company (2024) · 2024
Later among the works it cites.
Learning dexterity from human hand motion in internet videos
Shaw, K., Bahl, S., Sivakumar, A., Kannan, A., and Pathak, D. (2024) · 2024
Later among the works it cites.
Tilde: Teleoperation for dexterous in-hand manipulation learning with a deltahand
Si, Z., Zhang, K. L., Temel, Z., and Kroemer, O. (2024) · 2024
Later among the works it cites.
Kinematic synergy primitives for human-like grasp motion generation
Starke, J. and Asfour, T. (2024) · 2024
Later among the works it cites.
Deep generative models in robotics: A survey on learning from multimodal demonstrations
Urain, J., Mandlekar, A., Du, Y., Shafiullah, M., Xu, D., Fragkiadaki, K., Chalvatzaki, G., and Peters, J. (2024) · 2024
Later among the works it cites.
Behavioral learning of dish rinsing and scrubbing based on interruptive direct teaching considering assistance rate
Wakabayashi, S., Kawaharazuka, K., Okada, K., and Inaba, M. (2024) · 2024
Later among the works it cites.
Allegro hand v4.0 - allegro hand
WONIK ROBOTICS (2023) · 2024
Later among the works it cites.
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
Later among the works it cites.
Graingrasp: Dexterous grasp generation with fine-grained contact guidance
Zhao, F., Tsetserukou, D., and Liu, Q. (2024) · 2024
Later among the works it cites.
Gr00t n1: An open foundation model for generalist humanoid robots
Bjorck, J., Castañeda, F., Cherniadev, N., Da, X., Ding, R., Fan, L. J., Fang, Y., Fox, D., Hu, F., Huang, S., Jang, J., Jiang, Z., Kautz, J., Kundalia, K., Lao, L., Li, Z., Lin, Z., Lin, K., Liu, G., Llontop, E., Magne, L., Mandlekar, A., Narayan, A., Nasiriany, S., Reed, S., Tan, Y. L., Wang, G., Wang, Z., Wang, J., Wang, Q., Xiang, J., Xie, Y., Xu, Y., Xu, Z., Ye, S., Yu, Z., Zhang, A., Zhang, H., Zhao, Y., Zheng, R., and Zhu, Y. (2025) · 2025
Closest in time.
Diffusion for multi-embodiment grasping
Freiberg, R., Qualmann, A., Vien, N. A., and Neumann, G. (2025) · 2025
Closest in time.
Metamvuc: Active learning for sample-efficient sim-to-real domain adaptation in robotic grasping
Gilles, M., Furmans, K., and Rayyes, R. (2025) · 2025
Closest in time.
Vtdexmanip: a dataset and benchmark for visual-tactile pretraining and dexterous manipulation with reinforcement learning
Liu, Q., Cui, Y., Sun, Z., Li, G., Chen, J., and Ye, Q. (2025b) · 2025
Closest in time.
Paxini dexh13gen2
PaXini (2025) · 2025
Closest in time.
Dg-5f | humanoid robotic hand for dexterous manipulation
TESOLLO (2025) · 2025
Closest in time.
Unitree dex5-1 smart adaptability, instant responsiveness - unitree robotics
Unitree (2025) · 2025
Closest in time.
Diffusion models for robotic manipulation: A survey
Wolf, R., Shi, Y., Liu, S., and Rayyes, R. (2025) · 2025
Closest in time.
Robocopilot: Human-in-the-loop interactive imitation learning for robot manipulation
Wu, P., Shentu, Y., Liao, Q., Jin, D., Guo, M., Sreenath, K., Lin, X., and Abbeel, P. (2025) · 2025
Closest in time.
Flowpolicy: Enabling fast and robust 3d flow-based policy via consistency flow matching for robot manipulation
Zhang, Q., Liu, Z., Fan, H., Liu, G., Zeng, B., and Liu, S. (2025) · 2025
Closest in time.
Dexgraspvla: A vision-language-action framework towards general dexterous grasping
Zhong, Y., Huang, X., Li, R., Zhang, C., Liang, Y., Yang, Y., and Chen, Y. (2025) · 2025
Closest in time.
Generalization of human grasping for multi-fingered robot hands
Ben Amor, H., Kroemer, O., Hillenbrand, U., Neumann, G., and Peters, J. (2012) · 2050
Closest in time.