Fetching the paper…
Reading the bibliography…
This paper presents a differential geometric control approach that leverages SE(3) group invariance and equivariance to increase transferability in learning robot manipulation tasks that involve interaction with the environment.
O. Khatib, “A unified approach for motion and force control of robot manipulators: The operational space formulation,” IEEE Journal on Robotics and Automation , vol. 3, no. 1, pp. 43–53, 1987
1987
Earlier work this paper cites.
R. M. Murray, Z. Li, and S. S. Sastry, A mathematical introduction to robotic manipulation . CRC press, 1994
1994
Earlier work this paper cites.
F. Bullo and R. M. Murray, “Tracking for fully actuated mechanical systems: a geometric framework,” Automatica , vol. 35, no. 1, pp. 17–34, 1999
1999
Earlier work this paper cites.
F. Caccavale et al. , “Six-dof impedance control based on angle/axis representations,” IEEE Transactions on Robotics and Automation , vol. 15, no. 2, pp. 289–300, 1999
1999
Earlier work this paper cites.
T. Lee et al. , “Geometric tracking control of a quadrotor uav on SE(3),” in 49th IEEE conference on decision and control (CDC) . IEEE, 2010, pp. 5420–5425
2010
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2012, pp. 5026–5033
2012
Earlier work this paper cites.
T. Cohen and M. Welling, “Group equivariant convolutional networks,” in International conference on machine learning . PMLR, 2016, pp. 2990–2999
2016
Earlier work this paper cites.
T. Inoue et al. , “Deep reinforcement learning for high precision assembly tasks,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 819–825
2017
Earlier work this paper cites.
K. M. Lynch and F. C. Park, Modern robotics . Cambridge University Press, 2017
2017
Earlier work this paper cites.
J. Tobin et al. , “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
2017
Earlier work this paper cites.
E. J. Bekkers et al. , “Roto-translation covariant convolutional networks for medical image analysis,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I . Springer, 2018, pp. 440–448
2018
Earlier work this paper cites.
H. Ravichandar et al. , “Recent advances in robot learning from demonstration,” Annual review of control, robotics, and autonomous systems , vol. 3, pp. 297–330, 2020
2020
Cited alongside, same era.
C. C. Beltran-Hernandez et al. , “Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach,” Applied Sciences , vol. 10, no. 19, p. 6923, 2020
2020
Cited alongside, same era.
V. der Pol et al. , “MDP homomorphic networks: Group symmetries in reinforcement learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 4199–4210, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Kozlovsky, E. Newman, and M. Zacksenhouse, “Reinforcement learning of impedance policies for peg-in-hole tasks: Role of asymmetric matrices,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 10 898–10 905, 2022
2022
Later among the works it cites.
S. Shaw, B. Abbatematteo, and G. Konidaris, “RMPs for safe impedance control in contact-rich manipulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2707–2713
2022
Later among the works it cites.
J. Seo et al. , “Geometric impedance control on SE(3) for robotic manipulators,” IFAC World Congress 2023, Yokohama, Japan , 2023
2023
Closest in time.
H. Ryu et al. , “Equivariant descriptor fields: SE(3)-equivariant energy-based models for end-to-end visual robotic manipulation learning,” in The Eleventh International Conference on Learning Representations (ICLR) , 2023
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Zhang et al. , “Learning variable impedance control via inverse reinforcement learning for force-related tasks,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 2225–2232, 2021
2021
Cited alongside, same era.
A. Zeng et al. , “Transporter networks: Rearranging the visual world for robotic manipulation,” in Conference on Robot Learning . PMLR, 2021, pp. 726–747
2021
Cited alongside, same era.
H. Ochoa and R. Cortesão, “Impedance control architecture for robotic-assisted mold polishing based on human demonstration,” IEEE Transactions on Industrial Electronics , vol. 69, no. 4, pp. 3822–3830, 2021
2021
Cited alongside, same era.
A. Simeonov et al. , “Neural descriptor fields: SE(3)-equivariant object representations for manipulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6394–6400
2022
Cited alongside, same era.
H. Ha and S. Song, “Flingbot: The unreasonable effectiveness of dynamic manipulation for cloth unfolding,” in Conference on Robot Learning . PMLR, 2022, pp. 24–33
2022
Cited alongside, same era.
D. Wang et al. , “Equivariant q q learning in spatial action spaces,” in Conference on Robot Learning . PMLR, 2022, pp. 1713–1723
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Closest in time.
J. Kim et al. , “Robotic manipulation learning with equivariant descriptor fields: Generative modeling, bi-equivariance, steerability, and locality,” in RSS 2023 Workshop on Symmetries in Robot Learning , 2023
2023
Closest in time.
C. Pan et al. , “Tax-pose: Task-specific cross-pose estimation for robot manipulation,” in Conference on Robot Learning . PMLR, 2023, pp. 1783–1792
2023
Closest in time.
S. Kim et al. , “SE(2)-equivariant pushing dynamics models for tabletop object manipulations,” in Conference on Robot Learning . PMLR, 2023, pp. 427–436
2023
Closest in time.
“Berkeley RL Kit,” https://github.com/rail-berkeley/rlkit
2023
Closest in time.