Built on

R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,” Neural computation , vol. 3, no. 1, pp. 79–87, 1991
1991
Earlier work this paper cites.
M. I. Jordan and R. A. Jacobs, “Hierarchical mixtures of experts and the em algorithm,” Neural computation , vol. 6, no. 2, pp. 181–214, 1994
1994
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning , vol. 28, pp. 41–75, 1997
1997
Earlier work this paper cites.
R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of the 25th international conference on Machine learning , 2008, pp. 160–167
2008
Earlier work this paper cites.
M. Deisenroth and J. W. Ng, “Distributed gaussian processes,” in International conference on machine learning . PMLR, 2015, pp. 1481–1490
2015
Earlier work this paper cites.
P. Liu, X. Qiu, and X. Huang, “Recurrent neural network for text classification with multi-task learning,” arXiv preprint arXiv:1605.05101 , 2016
Original
2016
Earlier work this paper cites.
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel, “Rl 2 : Fast reinforcement learning via slow reinforcement learning,” arXiv preprint arXiv:1611.02779 , 2016
Original
2016
Earlier work this paper cites.
L. Pinto and A. Gupta, “Learning to push by grasping: Using multiple tasks for effective learning,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 2161–2168
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning . PMLR, 2017, pp. 1126–1135
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017
Original
2017
Earlier work this paper cites.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
2019
Earlier work this paper cites.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, p. eabc5986, 2020
2020
Earlier work this paper cites.
T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn, “Gradient surgery for multi-task learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 5824–5836, 2020
2020
Earlier work this paper cites.
C. Yang, K. Yuan, Q. Zhu, W. Yu, and Z. Li, “Multi-expert learning of adaptive legged locomotion,” Science Robotics , vol. 5, no. 49, p. eabb2174, 2020
2020
Earlier work this paper cites.
C. Yang, K. Yuan, Q. Zhu, W. Yu, and Z. Li, “Multi-expert learning of adaptive legged locomotion,” Science Robotics , vol. 5, no. 49, p. eabb2174, 2020
2020
Earlier work this paper cites.
D. Lepikhin, H. Lee, Y. Xu, D. Chen, O. Firat, Y. Huang, M. Krikun, N. Shazeer, and Z. Chen, “Gshard: Scaling giant models with conditional computation and automatic sharding,” arXiv preprint arXiv:2006.16668 , 2020
Original
2020
Earlier work this paper cites.
A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” arXiv preprint arXiv:2107.04034 , 2021
Original
2021
Earlier work this paper cites.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, et al. , “Isaac gym: High performance gpu-based physics simulation for robot learning,” arXiv preprint arXiv:2108.10470 , 2021
Original
2021
Earlier work this paper cites.

Similar

S. Vandenhende, S. Georgoulis, W. Van Gansbeke, M. Proesmans, D. Dai, and L. Van Gool, “Multi-task learning for dense prediction tasks: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 7, pp. 3614–3633, 2021
2021
Cited alongside, same era.
B. Liu, X. Liu, X. Jin, P. Stone, and Q. Liu, “Conflict-averse gradient descent for multi-task learning,” Advances in Neural Information Processing Systems , vol. 34, pp. 18 878–18 890, 2021
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Cited alongside, same era.
S. Sodhani, A. Zhang, and J. Pineau, “Multi-task reinforcement learning with context-based representations,” in International Conference on Machine Learning . PMLR, 2021, pp. 9767–9779
2021
Cited alongside, same era.
G. Ji, J. Mun, H. Kim, and J. Hwangbo, “Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4630–4637, 2022
2022
Cited alongside, same era.

Then

S. Luo, S. Li, R. Yu, Z. Wang, J. Wu, and Q. Zhu, “Pie: Parkour with implicit-explicit learning framework for legged robots,” arXiv preprint arXiv:2408.13740 , 2024
Original
2024
Later among the works it cites.
X. Cheng, K. Shi, A. Agarwal, and D. Pathak, “Extreme parkour with legged robots,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 11 443–11 450
2024
Later among the works it cites.
J. Obando-Ceron, G. Sokar, T. Willi, C. Lyle, J. Farebrother, J. Foerster, G. K. Dziugaite, D. Precup, and P. S. Castro, “Mixtures of experts unlock parameter scaling for deep rl,” arXiv preprint arXiv:2402.08609 , 2024
Original
2024
Later among the works it cites.
K. Li, M. Cucuringu, L. Sánchez-Betancourt, and T. Willi, “Mixtures of experts for scaling up neural networks in order execution,” in Proceedings of the 5th ACM International Conference on AI in Finance , 2024, pp. 669–676
2024
Later among the works it cites.

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2025

MoE-Loco: Mixture of Experts for Multitask Locomotion

Huang, Runhan, Zhu, Shaoting, Du, Yilun et al.

Understand

We present MoE-Loco, a Mixture of Experts (MoE) framework for multitask locomotion for legged robots.

Our method enables a single policy to handle diverse terrains, including bars, pits, stairs, slopes, and baffles, while supporting quadrupedal and bipedal gaits.
Using MoE, we mitigate the gradient conflicts that typically arise in multitask reinforcement learning, improving both training efficiency and performance.
Our experiments demonstrate that different experts naturally specialize in distinct locomotion behaviors, which can be leveraged for task migration and skill composition.

Built on

R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, “Adaptive mixtures of local experts,” Neural computation , vol. 3, no. 1, pp. 79–87, 1991
1991
Earlier work this paper cites.
M. I. Jordan and R. A. Jacobs, “Hierarchical mixtures of experts and the em algorithm,” Neural computation , vol. 6, no. 2, pp. 181–214, 1994
1994
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning , vol. 28, pp. 41–75, 1997
1997
Earlier work this paper cites.
R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proceedings of the 25th international conference on Machine learning , 2008, pp. 160–167
2008
Earlier work this paper cites.
M. Deisenroth and J. W. Ng, “Distributed gaussian processes,” in International conference on machine learning . PMLR, 2015, pp. 1481–1490
2015
Earlier work this paper cites.
P. Liu, X. Qiu, and X. Huang, “Recurrent neural network for text classification with multi-task learning,” arXiv preprint arXiv:1605.05101 , 2016
Original
2016
Earlier work this paper cites.
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel, “Rl 2 : Fast reinforcement learning via slow reinforcement learning,” arXiv preprint arXiv:1611.02779 , 2016
Original
2016
Earlier work this paper cites.
L. Pinto and A. Gupta, “Learning to push by grasping: Using multiple tasks for effective learning,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 2161–2168
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning . PMLR, 2017, pp. 1126–1135
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017
Original
2017
Earlier work this paper cites.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, p. eaau5872, 2019
2019
Earlier work this paper cites.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, p. eabc5986, 2020
2020
Earlier work this paper cites.
T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn, “Gradient surgery for multi-task learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 5824–5836, 2020
2020
Earlier work this paper cites.
C. Yang, K. Yuan, Q. Zhu, W. Yu, and Z. Li, “Multi-expert learning of adaptive legged locomotion,” Science Robotics , vol. 5, no. 49, p. eabb2174, 2020
2020
Earlier work this paper cites.
C. Yang, K. Yuan, Q. Zhu, W. Yu, and Z. Li, “Multi-expert learning of adaptive legged locomotion,” Science Robotics , vol. 5, no. 49, p. eabb2174, 2020
2020
Earlier work this paper cites.
D. Lepikhin, H. Lee, Y. Xu, D. Chen, O. Firat, Y. Huang, M. Krikun, N. Shazeer, and Z. Chen, “Gshard: Scaling giant models with conditional computation and automatic sharding,” arXiv preprint arXiv:2006.16668 , 2020
Original
2020
Earlier work this paper cites.
A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” arXiv preprint arXiv:2107.04034 , 2021
Original
2021
Earlier work this paper cites.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, et al. , “Isaac gym: High performance gpu-based physics simulation for robot learning,” arXiv preprint arXiv:2108.10470 , 2021
Original
2021
Earlier work this paper cites.

Similar

S. Vandenhende, S. Georgoulis, W. Van Gansbeke, M. Proesmans, D. Dai, and L. Van Gool, “Multi-task learning for dense prediction tasks: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 7, pp. 3614–3633, 2021
2021
Cited alongside, same era.
B. Liu, X. Liu, X. Jin, P. Stone, and Q. Liu, “Conflict-averse gradient descent for multi-task learning,” Advances in Neural Information Processing Systems , vol. 34, pp. 18 878–18 890, 2021
2021
Cited alongside, same era.
S. Sodhani, A. Zhang, and J. Pineau, “Multi-task reinforcement learning with context-based representations,” in International Conference on Machine Learning . PMLR, 2021, pp. 9767–9779
2021
Cited alongside, same era.
G. Ji, J. Mun, H. Kim, and J. Hwangbo, “Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4630–4637, 2022
2022
Cited alongside, same era.

Then

S. Luo, S. Li, R. Yu, Z. Wang, J. Wu, and Q. Zhu, “Pie: Parkour with implicit-explicit learning framework for legged robots,” arXiv preprint arXiv:2408.13740 , 2024
Original
2024
Later among the works it cites.
X. Cheng, K. Shi, A. Agarwal, and D. Pathak, “Extreme parkour with legged robots,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 11 443–11 450
2024
Later among the works it cites.
J. Obando-Ceron, G. Sokar, T. Willi, C. Lyle, J. Farebrother, J. Foerster, G. K. Dziugaite, D. Precup, and P. S. Castro, “Mixtures of experts unlock parameter scaling for deep rl,” arXiv preprint arXiv:2402.08609 , 2024
Original
2024
Later among the works it cites.
K. Li, M. Cucuringu, L. Sánchez-Betancourt, and T. Willi, “Mixtures of experts for scaling up neural networks in order execution,” in Proceedings of the 5th ACM International Conference on AI in Finance , 2024, pp. 669–676
2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

S. Zhou, W. Zhang, J. Jiang, W. Zhong, J. Gu, and W. Zhu, “On the convergence of stochastic multi-objective gradient manipulation and beyond,” Advances in Neural Information Processing Systems , vol. 35, pp. 38 103–38 115, 2022

2022

Cited alongside, same era.

K. N. Kumar, I. Essa, and S. Ha, “Cascaded compositional residual learning for complex interactive behaviors,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 4601–4608, 2023

2023

Cited alongside, same era.

A. Klipfel, N. Sontakke, R. Liu, and S. Ha, “Learning a single policy for diverse behaviors on a quadrupedal robot using scalable motion imitation,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 2768–2775

2023

Cited alongside, same era.

Z. Zhuang, Z. Fu, J. Wang, C. Atkeson, S. Schwertfeger, C. Finn, and H. Zhao, “Robot parkour learning,” arXiv preprint arXiv:2309.05665 , 2023

Original

2023

Cited alongside, same era.

J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters , 2023

2023

Cited alongside, same era.

S. Liu, Z. Chen, Y. Liu, Y. Wang, D. Yang, Z. Zhao, Z. Zhou, X. Yi, W. Li, W. Zhang, et al. , “Improving generalization in visual reinforcement learning via conflict-aware gradient agreement augmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 23 436–23 446

2023

Cited alongside, same era.

M. Mittal, C. Yu, Q. Yu, J. Liu, N. Rudin, D. Hoeller, J. L. Yuan, R. Singh, Y. Guo, H. Mazhar, A. Mandlekar, B. Babich, G. State, M. Hutter, and A. Garg, “Orbit: A unified simulation framework for interactive robot learning environments,” IEEE Robotics and Automation Letters , vol. 8, no. 6, pp. 3740–3747, 2023

2023

Cited alongside, same era.

L. Smith, J. C. Kew, T. Li, L. Luu, X. B. Peng, S. Ha, J. Tan, and S. Levine, “Learning and adapting agile locomotion skills by transferring experience,” arXiv preprint arXiv:2304.09834 , 2023

Original

2023

Cited alongside, same era.

O. Celik, A. Taranovic, and G. Neumann, “Acquiring diverse skills using curriculum reinforcement learning with mixture of experts,” arXiv preprint arXiv:2403.06966 , 2024

Original

2024