Fetching the paper…
Reading the bibliography…
Evolution in nature illustrates that the creatures' biological structure and their sensorimotor skills adapt to the environmental changes for survival.
J. Napier, “The evolution of the hand,” Scientific American , 1962
1962
Earlier work this paper cites.
C. Leger et al. , Automated synthesis and optimization of robot configurations: an evolutionary approach , 1999
1999
Earlier work this paper cites.
C. Mautner and R. K. Belew, “Evolving robot morphology and control,” Artificial Life and Robotics , vol. 4, no. 3, pp. 130–136, 2000
2000
Earlier work this paper cites.
R. W. Young, “Evolution of the human hand: the role of throwing and clubbing,” Journal of Anatomy , vol. 202, no. 1, pp. 165–174, 2003
2003
Earlier work this paper cites.
N. Hansen, S. D. Müller, and P. Koumoutsakos, “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es),” Evolutionary computation , vol. 11, no. 1, pp. 1–18, 2003
2003
Earlier work this paper cites.
R. Pfeifer and G. Gómez, “Morphological computation–connecting brain, body, and environment,” in Creating brain-like intelligence . Springer, 2009, pp. 66–83
2009
Earlier work this paper cites.
K. Wampler and Z. Popović, “Optimal gait and form for animal locomotion,” ACM Transactions on Graphics (TOG) , vol. 28, no. 3, pp. 1–8, 2009
2009
Earlier work this paper cites.
E. Van Henten, D. Van’t Slot, C. Hol, and L. Van Willigenburg, “Optimal manipulator design for a cucumber harvesting robot,” Computers and electronics in agriculture , vol. 65, no. 2, pp. 247–257, 2009
2009
Earlier work this paper cites.
N. Cheney, R. MacCurdy, J. Clune, and H. Lipson, “Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding,” in Proceedings of the 15th annual conference on Genetic and evolutionary computation , 2013, pp. 167–174
2013
Earlier work this paper cites.
J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis—a survey,” IEEE Transactions on Robotics , vol. 30, no. 2, pp. 289–309, 2013
2013
Earlier work this paper cites.
S. Seok, A. Wang, M. Y. Chuah, D. J. Hyun, J. Lee, D. M. Otten, J. H. Lang, and S. Kim, “Design principles for energy-efficient legged locomotion and implementation on the mit cheetah robot,” Ieee/asme transactions on mechatronics , vol. 20, no. 3, pp. 1117–1129, 2014
2014
Earlier work this paper cites.
T. Feix, J. Romero, H.-B. Schmiedmayer, A. M. Dollar, and D. Kragic, “The GRASP taxonomy of human grasp types,” IEEE Transactions on human-machine systems , vol. 46, no. 1, pp. 66–77, 2015
2015
Earlier work this paper cites.
K. Graichen, S. Hentzelt, A. Hildebrandt, N. Kärcher, N. Gaißert, and E. Knubben, “Control design for a bionic kangaroo,” Control Engineering Practice , vol. 42, pp. 106–117, 2015
2015
Earlier work this paper cites.
I. Lenz, H. Lee, and A. Saxena, “Deep learning for detecting robotic grasps,” The International Journal of Robotics Research , vol. 34, no. 4-5, pp. 705–724, 2015
2015
Earlier work this paper cites.
S. B. Backus and A. M. Dollar, “An adaptive three-fingered prismatic gripper with passive rotational joints,” IEEE Robotics and Automation Letters , vol. 1, no. 2, pp. 668–675, 2016
2016
Earlier work this paper cites.
R. Deimel and O. Brock, “A novel type of compliant and underactuated robotic hand for dexterous grasping,” The International Journal of Robotics Research , vol. 35, no. 1-3, pp. 161–185, 2016
2016
Cited alongside, same era.
L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in IEEE International Conference on Robotics and Automation (ICRA) , 2016
2016
Cited alongside, same era.
A. Gupta, C. Eppner, S. Levine, and P. Abbeel, “Learning dexterous manipulation for a soft robotic hand from human demonstrations,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 3786–3793
2016
Cited alongside, same era.
2017
Cited alongside, same era.
J. Dhamala, S. Ghimire, J. L. Sapp, B. M. Horáček, and L. Wang, “High-dimensional bayesian optimization of personalized cardiac model parameters via an embedded generative model,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 499–507
2018
Later among the works it cites.
2019
Later among the works it cites.
A. Spielberg, A. Zhao, Y. Hu, T. Du, W. Matusik, and D. Rus, “Learning-in-the-loop optimization: End-to-end control and co-design of soft robots through learned deep latent representations,” in Advances in Neural Information Processing Systems 32 , 2019
2019
Later among the works it cites.
Y. Hu, J. Liu, A. Spielberg, J. B. Tenenbaum, W. T. Freeman, J. Wu, D. Rus, and W. Matusik, “Chainqueen: A real-time differentiable physical simulator for soft robotics,” in International Conference on Robotics and Automation (ICRA) , 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
C. Eppner, S. Höfer, R. Jonschkowski, A. Sieverling, V. Wall, and O. Brock, “Lessons from the amazon picking challenge: Four aspects of building robotic systems,” in h International Joint Conference on Artificial Intelligence , 2017
2017
Cited alongside, same era.
S. Ha, S. Coros, A. Alspach, J. Kim, and K. Yamane, “Joint optimization of robot design and motion parameters using the implicit function theorem,” in RSS , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation in robotics, games and machine learning,” 2017
2017
Cited alongside, same era.
P. I. Frazier, “A tutorial on bayesian optimization,” arXiv preprint arXiv:1807.02811 , 2018
2018
Cited alongside, same era.
T. Chen, A. Murali, and A. Gupta, “Hardware conditioned policies for multi-robot transfer learning,” in Advances in Neural Information Processing Systems , 2018, pp. 9355–9366
2018
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
D. Pathak, C. Lu, T. Darrell, P. Isola, and A. A. Efros, “Learning to control self-assembling morphologies: a study of generalization via modularity,” in Advances in Neural Information Processing Systems , 2019, pp. 2295–2305
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Yuan, A. D. Epps, J. B. Nowak, and J. K. Salisbury, “Design of a roller-based dexterous hand for object grasping and within-hand manipulation,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2020
2020
Closest in time.
2020
Closest in time.
K. S. Luck, H. B. Amor, and R. Calandra, “Data-efficient co-adaptation of morphology and behaviour with deep reinforcement learning,” in Conference on Robot Learning , 2020, pp. 854–869
2020
Closest in time.
2020
Closest in time.
W. Huang, I. Mordatch, and D. Pathak, “One policy to control them all: Shared modular policies for agent-agnostic control,” in ICML , 2020
2020
Closest in time.
O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, et al. , “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 3–20, 2020
2020
Closest in time.
D. Morrison, P. Corke, and J. Leitner, “Egad! an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation,” IEEE Robotics and Automation Letters , 2020
2020
Closest in time.