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This arXiv preprint proposes a transformer-based deep reinforcement learning architecture with shared modular recurrence, aimed at one controller that generalizes across robot morphologies. The reported gains are zero-shot results on unseen MuJoCo robots across four environments, which is a useful signal but a simulation-only benchmark. Readers should watch whether the approach holds up on physical hardware and on morphologies far outside the training distribution.
Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
arXiv:2506.08630v4 Announce Type: replace Abstract: A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-robot control by utilizing contextual information about the properties of individual robots and exploiting their modular structure in the architecture of deep reinforcement learning agents. When the robots have highly dissimilar morphologies, however, this becomes a challenging problem, especially when the agent must generalize to new, unseen robots. In this paper, we consider the fact that contextual features are often only partially available, but that they can be recovered through modular interactions. This can allow for better multi-robot control and generalization to contexts that are not seen during training. To this extent, we implement a transformer-based architecture with shared modular recurrence and evaluate its (generalization) performance on a large set of MuJoCo robots. The results show a substantial improvement in zero-shot generalization performance on robots with unseen dynamics, kinematics, and topologies, in four different environments.
Abstract:A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-robot control by utilizing contextual information about the properties of individual robots and exploiting their modular structure in the architecture of deep reinforcement learning agents. When the robots have highly dissimilar morphologies, however, this becomes a challenging problem, especially when the agent must generalize to new, unseen robots. In this paper, we consider the fact that contextual features are often only partially available, but that they can be recovered through modular interactions. This can allow for better multi-robot control and generalization to contexts that are not seen during training. To this extent, we implement a transformer-based architecture with shared modular recurrence and evaluate its (generalization) performance on a large set of MuJoCo robots. The results show a substantial improvement in zero-shot generalization performance on robots with unseen dynamics, kinematics, and topologies, in four different environments.
Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2506.08630 [cs.AI]
(or arXiv:2506.08630v4 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2506.08630 Focus to learn more arXiv-issued DOI via DataCite
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