Embodiment Meets Environment: Toward Context-Aware, Safe Physical Caregiving Robots
Physical caregiving robots must adapt to diverse users, tasks, environments, and embodiments. Existing systems are often tightly coupled to specific settings and lack explicit modeling of human interaction. This paper proposes E²-CARE, a framework that uses interaction templates and a unified 3D dynamic scene graph to achieve context-aware adaptation, enabling zero-shot safe reuse of skill templates across environments and robot embodiments. Evaluations in hundreds of simulated environments and real-world user studies demonstrate consistent adaptation.
[2606.28592] Embodiment Meets Environment: Toward Context-Aware, Safe Physical Caregiving Robots
[Submitted on 26 Jun 2026]
Title:Embodiment Meets Environment: Toward Context-Aware, Safe Physical Caregiving Robots
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Abstract:Physical caregiving robots need to assist different users with different tasks in diverse environments, and they come in many embodiments. While substantial progress has been made on individual caregiving tasks, most existing systems remain tightly coupled to specific environments and robot embodiments, and often do not explicitly model or constrain interactions around people, despite humans being special agents in the environment. This motivates a focus on adapting to context that emerges from the joint interaction between the environment and the robot's embodiment. We propose $E^2$-CARE, a framework that enables context-aware adaptation by representing primitive caregiving skills as interaction templates whose execution is reshaped online. $E^2$-CARE represents the environment, the robot, and the human within a unified 3D dynamic scene graph that models these interaction contexts explicitly, and synthesizes task-specific constraints to govern how each skill is executed. By enforcing these constraints at runtime, the same skill templates can be reused zero-shot and safely across diverse environments and robot embodiments. We evaluate $E^2$-CARE across four activities of daily living in hundreds of simulated household environments, including assistive home settings, and across diverse robot embodiments, and validate it through user studies on two caregiving tasks with two robots in various real-world environments. Results demonstrate consistent and successful adaptation across these environments and embodiments. Website: this https URL
Comments: RSS 2026
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.28592 [cs.RO]
(or arXiv:2606.28592v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.28592
arXiv-issued DOI via DataCite (pending registration)
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From: Zhanxin Wu [view email] [v1] Fri, 26 Jun 2026 20:36:26 UTC (13,251 KB)
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