E²-CARE uses dynamic 3D scene graphs and synthesized constraints to let the same caregiving skill templates run zero-shot and safely across different household environments and robot bodies.
Flair: Feeding via long-horizon acquisition of realistic dishes
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.RO 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
E-MPC is a model predictive control framework that uses a user interaction dynamics model to balance autonomy and engagement under workload constraints in robotic caregiving, evaluated via simulation and a user study.
JOIN decomposes bimanual joining into plan-drive-grasp phases and uses a VLM to let a mobile manipulator complete tasks with a pre-grasped anchor arm, achieving 19/20 success versus 14/20 for baselines on representative ADLs.
citing papers explorer
-
Embodiment Meets Environment: Toward Context-Aware, Safe Physical Caregiving Robots
E²-CARE uses dynamic 3D scene graphs and synthesized constraints to let the same caregiving skill templates run zero-shot and safely across different household environments and robot bodies.
-
Beyond Failure Recovery: An Engagement-Aware Human-in-the-loop Framework for Robotic Systems
E-MPC is a model predictive control framework that uses a user interaction dynamics model to balance autonomy and engagement under workload constraints in robotic caregiving, evaluated via simulation and a user study.
-
JOIN: Anchor-Grasp-Conditioned Joining via Opposition, Inference, and Navigation for Bimanual Assistive Manipulation
JOIN decomposes bimanual joining into plan-drive-grasp phases and uses a VLM to let a mobile manipulator complete tasks with a pre-grasped anchor arm, achieving 19/20 success versus 14/20 for baselines on representative ADLs.