Ashutosh Mishra

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IEEE iSpaRo2025

Multi-Modal Decentralized Reinforcement Learning for Modular Reconfigurable Lunar Robots

Ashutosh Mishra, Shreya Santra, Elian Neppel, Edoardo M. Rossi Lombardi, Shamistan Karimov, Kentaro Uno, Kazuya Yoshida

IEEE International Conference on Space Robotics (iSpaRo)

A decentralized reinforcement learning approach for robots whose morphology changes: each module learns to act on local, multi-modal observation rather than from a central controller, so the same policy set survives reconfiguration.

doi:10.1109/ISPARO66239.2025.11436148arXiv preprintVideo

Best Paper Award in Planetary Robotics, IEEE iSpaRo 2025, Sendai, Japan

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IEEE

A. Mishra, S. Santra, E. Neppel, E. M. Rossi Lombardi, S. Karimov, K. Uno, et al., "Multi-Modal Decentralized Reinforcement Learning for Modular Reconfigurable Lunar Robots," in IEEE International Conference on Space Robotics (iSpaRo), 2025. doi: 10.1109/ISPARO66239.2025.11436148.

BibTeX
@inproceedings{mishra2025multimodal,
  author    = {Ashutosh Mishra and Shreya Santra and Elian Neppel and Edoardo M. Rossi Lombardi and Shamistan Karimov and Kentaro Uno and Kazuya Yoshida},
  title     = {{Multi-Modal Decentralized Reinforcement Learning for Modular Reconfigurable Lunar Robots}},
  booktitle = {IEEE International Conference on Space Robotics (iSpaRo)},
  year      = {2025},
  doi       = {10.1109/ISPARO66239.2025.11436148},
  note      = {Best Paper Award in Planetary Robotics, IEEE iSpaRo 2025, Sendai, Japan}
}

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