Fetching the paper…
Reading the bibliography…
Challenges in real-world robotic applications often stem from managing multiple, dynamically varying entities such as neighboring robots, manipulable objects, and navigation goals.
T. Ming, “Multi-agent reinforcement learning: Independent versus cooperative agents,” in
1993
Earlier work this paper cites.
S. Sukhbaatar, R. Fergus
2016
Earlier work this paper cites.
J. Foerster, I. A. Assael, N. De Freitas, and S. Whiteson, “Learning to communicate with deep multi-agent reinforcement learning,”
2016
Earlier work this paper cites.
J. Mahler and K. Goldberg, “Learning deep policies for robot bin picking by simulating robust grasping sequences,” in
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”
2017
Earlier work this paper cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mordatch, “Multi-agent actor-critic for mixed cooperative-competitive environments,”
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P.-W. Chou, D. Maturana, and S. Scherer, “Improving stochastic policy gradients in continuous control with deep reinforcement learning using the beta distribution,” in
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
W. Jakob, J. Rhinelander, and D. Moldovan, “pybind11 – seamless operability between c++11 and python,” 2017, https://github.com/pybind/pybind11
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Y. Yang, R. Luo, M. Li, M. Zhou, W. Zhang, and J. Wang, “Mean field multi-agent reinforcement learning,” in
2018
Earlier work this paper cites.
J. Jiang and Z. Lu, “Learning attentional communication for multi-agent cooperation,”
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
F. Wang and K. Hauser, “Stable bin packing of non-convex 3d objects with a robot manipulator,” in
2019
Earlier work this paper cites.
Y. Lee, J. Yang, and J. J. Lim, “Learning to coordinate manipulation skills via skill behavior diversification,” in
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
M. Bjelonic, C. D. Bellicoso, Y. de Viragh, D. Sako, F. D. Tresoldi, F. Jenelten, and M. Hutter, “Keep rollin’—whole-body motion control and planning for wheeled quadrupedal robots,”
2019
Earlier work this paper cites.
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, “Dynamic graph cnn for learning on point clouds,”
2019
Earlier work this paper cites.
J. Lee, Y. Lee, J. Kim, A. Kosiorek, S. Choi, and Y. W. Teh, “Set transformer: A framework for attention-based permutation-invariant neural networks,” in
2019
Cited alongside, same era.
T. Chu, J. Wang, L. Codecà, and Z. Li, “Multi-agent deep reinforcement learning for large-scale traffic signal control,”
2019
Cited alongside, same era.
A. Das, T. Gervet, J. Romoff, D. Batra, D. Parikh, M. Rabbat, and J. Pineau, “Tarmac: Targeted multi-agent communication,” in
2019
Cited alongside, same era.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,”
2020
Cited alongside, same era.
R. Li, A. Jabri, T. Darrell, and P. Agrawal, “Towards practical multi-object manipulation using relational reinforcement learning,” in
2020
Cited alongside, same era.
C. Yang, G. N. Sue, Z. Li, L. Yang, H. Shen, Y. Chi, A. Rai, J. Zeng, and K. Sreenath, “Collaborative navigation and manipulation of a cable-towed load by multiple quadrupedal robots,”
2022
Later among the works it cites.
Q. Xu, J. Li, S. Koenig, and H. Ma, “Multi-goal multi-agent pickup and delivery,” in
2022
Later among the works it cites.
R. Zhang, G. Chen, J. Hou, Z. Li, and A. Knoll, “Pipo: Policy optimization with permutation-invariant constraint for distributed multi-robot navigation,” in
2022
Later among the works it cites.
S. Shaw, E. Wenzel, A. Walker, and G. Sartoretti, “Formic: Foraging via multiagent rl with implicit communication,”
2022
Later among the works it cites.
F. Schmalstieg, D. Honerkamp, T. Welschehold, and A. Valada, “Learning long-horizon robot exploration strategies for multi-object search in continuous action spaces,” in
2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. A. Hamed, V. R. Kamidi, A. Pandala, W.-L. Ma, and A. D. Ames, “Distributed feedback controllers for stable cooperative locomotion of quadrupedal robots: A virtual constraint approach,” in
2020
Cited alongside, same era.
H. Ha, J. Xu, and S. Song, “Learning a decentralized multi-arm motion planner,”
2020
Cited alongside, same era.
S. Zimmermann, G. Hakimifard, M. Zamora, R. Poranne, and S. Coros, “A multi-level optimization framework for simultaneous grasping and motion planning,”
2020
Cited alongside, same era.
A. Zeng, S. Song, J. Lee, A. Rodriguez, and T. Funkhouser, “Tossingbot: Learning to throw arbitrary objects with residual physics,”
2020
Cited alongside, same era.
2020
Cited alongside, same era.
T. Rashid, M. Samvelyan, C. S. De Witt, G. Farquhar, J. Foerster, and S. Whiteson, “Monotonic value function factorisation for deep multi-agent reinforcement learning,”
2020
Cited alongside, same era.
J. Wang, Z. Ren, T. Liu, Y. Yu, and C. Zhang, “Qplex: Duplex dueling multi-agent q-learning,”
2020
Cited alongside, same era.
Later among the works it cites.
C. Yu, A. Velu, E. Vinitsky, J. Gao, Y. Wang, A. Bayen, and Y. Wu, “The surprising effectiveness of ppo in cooperative multi-agent games,”
2022
Later among the works it cites.
2022
Later among the works it cites.
X. Zhou, X. Wen, Z. Wang, Y. Gao, H. Li, Q. Wang, T. Yang, H. Lu, Y. Cao, C. Xu
2022
Later among the works it cites.
J. Lee, M. Bjelonic, and M. Hutter, “Control of wheeled-legged quadrupeds using deep reinforcement learning,” in
2022
Later among the works it cites.
M. Tranzatto, T. Miki, M. Dharmadhikari, L. Bernreiter, M. Kulkarni, F. Mascarich, O. Andersson, S. Khattak, M. Hutter, R. Siegwart
2022
Later among the works it cites.
C. Chen, J. Frey, P. Arm, and M. Hutter, “Smug planner: A safe multi-goal planner for mobile robots in challenging environments,”
2023
Later among the works it cites.
H. Kondoh and A. Kanezaki, “Multi-goal audio-visual navigation using sound direction map,” in
2023
Later among the works it cites.
F. De Vincenti and S. Coros, “Centralized model predictive control for collaborative loco-manipulation,”
2023
Later among the works it cites.
F. Kennel-Maushart, R. Poranne, and S. Coros, “Interacting with multi-robot systems via mixed reality,” in
2023
Later among the works it cites.
gRPC, “About grpc,”
2023
Later among the works it cites.
Y. Huang, A. Conkey, and T. Hermans, “Planning for multi-object manipulation with graph neural network relational classifiers,” in
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Yu, X. Yang, J. Gao, J. Chen, Y. Li, J. Liu, Y. Xiang, R. Huang, H. Yang, Y. Wu
2023
Later among the works it cites.
F. De Vincenti and S. Coros, “Ungar - a c++ framework for real-time optimal control using template metaprogramming,” in
2023
Later among the works it cites.
protobuf.dev, “Protocol buffers documentation,”
2023
Later among the works it cites.
J. Envall, R. Poranne, and S. Coros, “Differentiable task assignment and motion planning,” in
2056
Closest in time.