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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.07117 |
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| _version_ | 1866914327034855424 |
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| author | Christov-Moore, Leonardo Juliani, Arthur Kiefer, Alex Lehman, Joel Reggente, Nicco Rousse, B. Scot Safron, Adam Hinrichs, Nicolás Polani, Daniel Damasio, Antonio |
| author_facet | Christov-Moore, Leonardo Juliani, Arthur Kiefer, Alex Lehman, Joel Reggente, Nicco Rousse, B. Scot Safron, Adam Hinrichs, Nicolás Polani, Daniel Damasio, Antonio |
| contents | As artificial agents enter open-ended physical environments -- eldercare, disaster response, and space missions -- they must persist under uncertainty while providing reliable care. Yet current systems struggle to generalize across distribution shifts and lack intrinsic motivation to preserve the well-being of others. Vulnerability and mortality are often seen as constraints to be avoided, yet organisms survive and provide care in an open-ended world with relative ease and efficiency. We argue that generalization and care arise from conditions of physical embodiment: being-in-the-world (the agent is a part of the environment) and being-towards-death (unless counteracted, the agent drifts toward terminal states). These conditions necessitate a homeostatic drive to maintain oneself and maximize the future capacity to continue doing so. Fulfilling this drive over long time horizons in multi-agent environments necessitates robust causal modeling of self and others' embodiment and jointly achievable future states. Because embodied agents are part of the environment, with the self delimited by reliable control, empowering others can expand self-boundaries, enabling other-regard. This provides a path from embodiment toward generalization and care based in shared constraints. We outline a reinforcement-learning framework for examining these questions. Homeostatic mortal agents continually learning in open-ended environments may offer efficient robustness and trustworthy alignment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Conditions of Physical Embodiment Enable Generalization and Care Christov-Moore, Leonardo Juliani, Arthur Kiefer, Alex Lehman, Joel Reggente, Nicco Rousse, B. Scot Safron, Adam Hinrichs, Nicolás Polani, Daniel Damasio, Antonio Artificial Intelligence Machine Learning As artificial agents enter open-ended physical environments -- eldercare, disaster response, and space missions -- they must persist under uncertainty while providing reliable care. Yet current systems struggle to generalize across distribution shifts and lack intrinsic motivation to preserve the well-being of others. Vulnerability and mortality are often seen as constraints to be avoided, yet organisms survive and provide care in an open-ended world with relative ease and efficiency. We argue that generalization and care arise from conditions of physical embodiment: being-in-the-world (the agent is a part of the environment) and being-towards-death (unless counteracted, the agent drifts toward terminal states). These conditions necessitate a homeostatic drive to maintain oneself and maximize the future capacity to continue doing so. Fulfilling this drive over long time horizons in multi-agent environments necessitates robust causal modeling of self and others' embodiment and jointly achievable future states. Because embodied agents are part of the environment, with the self delimited by reliable control, empowering others can expand self-boundaries, enabling other-regard. This provides a path from embodiment toward generalization and care based in shared constraints. We outline a reinforcement-learning framework for examining these questions. Homeostatic mortal agents continually learning in open-ended environments may offer efficient robustness and trustworthy alignment. |
| title | The Conditions of Physical Embodiment Enable Generalization and Care |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.07117 |