Active Perception of Upper-Limb Functional Mobility through Human-Robot Handovers

Francesco Semeraro; Marta Lagomarsino; Alberto Ranavolo; Arash Ajoudani

cs.ROOct 14, 2026

Active Perception of Upper-Limb Functional Mobility through Human-Robot Handovers

Authors: Francesco Semeraro, Marta Lagomarsino, Alberto Ranavolo, Arash Ajoudani

Organizations: Human-Robot Interfaces and Interaction (HRI2) Laboratory, Italian Institute of Technology, 16163, Genoa, Italy. · Department of Occupational and Environmental Medicine, Epidemiology and Hygiene, INAIL, 00078, Rome, Italy.

Abstract

The reintegration of people in the workplace after an upper-limb injury is often a long process. Collaborative robots and humanoids operating in shared workspaces must adapt their behaviour to the physical capabilities of their human partners. Achieving this requires robots to autonomously estimate task-relevant human mobility during interaction, without relying on external clinical assessments. In this work, we formulate the estimation of upper-limb functional mobility as an active perception problem. We model task-available upper-limb functional ranges of motion through impairment coefficients that parameterise reductions of healthy joint limits, and integrate this representation into a human model used by the robot during interaction. Based on this formulation, we propose an online active perception pipeline in which the robot actively probes the user through repeated handover requests while progressively updating a Bayesian belief over the impairment coefficients. This enables the robot to continuously refine its internal model of the user's joint-level capabilities through interaction rather than passive observation. The proposed framework was evaluated through simulation studies and a proof-of-concept physical validation with a robotic platform and enacted human impairments, demonstrating that the pipeline can accurately recover the impairment coefficients during real human-robot handovers.

Figures & tables

Fig. 1: A person with an impairment enacted by means of a harness performs a handover requested by the robot. An underlying optimisation process computes the inverse kinematics of the human arm to the best extent allowed by the current belief on the impairment. This last gets updated based on the mismatch between the estimate and the real upper-limb joint values measured during the interaction.

Fig. 2: Diagram of the active perception pipeline for fROM estimation.

Fig. 3: Kinematic chain and rendering of the human model.

Fig. 4: Pictures from the physical validations with enacted impairments. From left to right: $\alpha_{S_{y}}^{*}=1.0$ , $\alpha_{S_{y}}^{*}=0.5$ , $\alpha_{E_{y}}^{*}=1.0$ and $\alpha_{E_{y}}^{*}=0.5$ . In some instances of the interactions, the participant was not able to reach the object. This is expected, as the pipeline does not have prior knowledge of the user’s real impairment. Nonetheless, the pipeline is able to perform the updates, because the underlying optimisation used enforces the constraint $\bm{x}_{obj}(\bm{q})=\bm{h}_{t}$ at the best extent allowed by the impairment, but it is able to return an IK solution even when the constraint cannot be met (see Subsection II-B ).

Fig. 5: Rendering of the experimental setting in Rviz 2. The human model was being acquired through the Real-Move setup. The green cube represents the action space the robot ould requests handovers within. The orange cube centered in the frame with the blue z axis oriented towards the user’s head represents $\bm{h}_{t}$ and the mug handle used in the handovers.

Configuration	Error		N. of Handovers
	Mean	SD	Mean	SD
$\alpha_{S_{y}}^{}\leq 0.5$ , $\alpha_{E_{y}}^{}\leq 0.5$	0.0867	0.0867	23.86	3.51
$\alpha_{S_{y}}^{}\leq 0.5$ , $\alpha_{E_{y}}^{}>0.5$	0.0504	0.0582	21.56	5.84
$\alpha_{S_{y}}^{}>0.5$ , $\alpha_{E_{y}}^{}\leq 0.5$	0.0833	0.0615	23.62	4.11
$\alpha_{S_{y}}^{}>0.5$ , $\alpha_{E_{y}}^{}>0.5$	0.0453	0.0590	19.91	6.80

TABLE I: Performance metrics across shoulder (S) and elbow (E) flexion impairment configurations. Each configuration was evaluated over 400 trials.

Fig. 6: Trends on the joint estimate of $\alpha_{S_{y}}^{*}$ and $\alpha_{E_{y}}^{*}$ in the physical validations. They all start from 0.5 because of the way $\mathcal{A}_{t}$ was defined (see Subsection II-C ). The shaded areas were plotted based on the related $\bm{\sigma_{\bm{\alpha},t}}$ . Trends terminating before iteration 25 indicate that the run was able to meet the early termination criterion (see Subsection II-C ).

Fig. 7: Visual comparisons between the result of the IK solution given by the $\bm{\hat{\bm{\alpha}}}$ at the current iteration and the recorded joint values (shaded mannequin) of the position performed by the participant during the handover.

Active Perception

Active perception studies how agents choose actions and viewpoints to gather useful sensory evidence while acting under uncertainty.

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Active Contact Sensing for Robust Robot-to-Human Object Handover

Linfeng Li, Lin Shao, David Hsu

Active Sensing Robotic Manipulation Human-Robot Interaction Human-Object Interaction

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Robot-to-human object handover is an essential skill for robot assistants, from serving drinks at home to passing surgical tools in the operating room. We expect robots to perform handover robustly -- to release the object only after a firm human grasp while ignoring incidental touches. Existing passive-sensing methods struggle to generalize across diverse objects and human behaviors, as they lack informative perturbations to disambiguate different contact conditions, such as firm grasp versus incidental touch. We propose an active sensing approach for robust handovers: the robot applies information-gathering motions and senses the resulting human-applied forces to infer the contact state. A firm grasp produces forces in multiple directions, while an accidental touch does not. To capture this distinction, we model the contact state with a Bayesian linear model: a distribution over piecewise-linear mappings from robot motions to human-applied forces. This model enables firm grasp detection and active information gathering. In experiments with 12 participants and 30 diverse rigid objects, our method achieved a 97.5% success rate -- over 30% higher than two common baselines.

Abstract

Active Perception

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