Abstract: Robots that physically interact with humans must decide not only how and when to help, but also when not to help. In physical caregiving and collaborative manipulation, robots can over-assist by misestimating user capability or defaulting to helping when users can act independently. Physical functionality is highly individual, only partially observable, difficult to specify a priori, and assistance policies are often not grounded in user-specific ability, making calibrated intervention challenging.
We address this by actively inferring human joint-space reachability from sparse interaction. Our framework represents reachability using a compositional parametric model where a box constraint is deformed by local Gaussian primitives. We learn a latent space that decodes to these parameters and structure it using biomechanical anchors from musculoskeletal simulation and clinical anchors from retrieval-augmented reasoning over rehabilitation literature. The robot maintains a belief over this manifold and actively selects calibration queries to infer user-specific functionality.
We evaluate through computational experiments and real-robot studies with participants wearing resistance bands. Our method achieves ≈0.50 IoU within 20 queries. In sandwich making, reachability-aware assistance significantly improves user perception of physical engagement (χ^2(3) = 18.29, p < .001) without increasing workload. In Action Research Arm Test-inspired manipulation, we demonstrate online adaptation capacity.