Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions

Wenqi Cai; John Abanes; Nikolaos Evangeliou; Anthony Tzes

cs.ROApr 13, 2026

Safe Human-to-Humanoid Motion Imitation Using Control Barrier Functions

Authors: Wenqi Cai, John Abanes, Nikolaos Evangeliou, Anthony Tzes

Organizations: The authors are with the Electrical Engineering Program, New York University Abu Dhabi (NYUAD), 129188, UAE. · The author is with the Electrical & Computer Engineering Department, New York University, Brooklyn, NY 11201, USA.

Abstract

Ensuring operational safety is critical for human-to-humanoid motion imitation. This paper presents a vision-based framework that enables a humanoid robot to imitate human movements while avoiding collisions. Human skeletal keypoints are captured by a single camera and converted into joint angles for motion retargeting. Safety is enforced through a Control Barrier Function (CBF) layer formulated as a Quadratic Program (QP), which filters imitation commands to prevent both self-collisions and human-robot collisions. Simulation results validate the effectiveness of the proposed framework for real-time collision-aware motion imitation.

Figures & tables

Fig. 1 : Overview of the proposed safe human-to-humanoid motion imitation framework.

Panel of Figure 2 — Fig. 2 : Self-collision avoidance with and without CBF-QP.

Panel of Figure 3 — Fig. 3 : Human-humanoid collision avoidance with and without CBF-QP.

Fig. 4 : Benchmarked collision geometry. From left to right: sphere decomposition with no subsampling, sphere decomposition with one subsampling, bounding box decomposition, capsule decomposition. Lines represent CBF constraints.

	CPU
Metric	Spheres (k=0)	Spheres (k=1)	Boxes	Capsule
Rate (Hz)	10.02	8.5	10.424	4.113
# Constraints	705	1988	72	72
Initial Compile Time (s)	0.02	0.05	0.4	0.7
Solve Time (s)	0.100	0.118	0.096	0.243
	GPU

TABLE I : CBF Benchmark Statistics for Various Collision Geometries

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Safety-critical control is essential for humanoid robots operating in complex human-centered environments, where physical safety constraints such as joint limits, self-collision avoidance, obstacle avoidance, and workspace boundaries must be satisfied during real-robot operation. However, existing approaches remain limited because kinematic safety guarantees can be degraded in the presence of unknown disturbances, such as model uncertainties, trajectory-tracking errors, and external perturbations. This paper presents a hierarchical safety-critical whole-body control framework for humanoid robots based on input-to-state safe control barrier functions (ISSf-CBFs). The proposed architecture integrates a kinematic-level whole-body controller (KinWBC), an ISSf-CBF safety filter, and a dynamic-level whole-body controller (DynWBC). KinWBC generates nominal joint-motion references from prioritized tasks; the ISSf-CBF filter minimally modifies these references to satisfy kinematic safety constraints under bounded disturbances; and DynWBC tracks the filtered references while enforcing full-body dynamic feasibility and contact stability. Safety constraints are imposed on a whole-body kinematic model, and the ISSf-CBF parameters are conservatively tuned so that the resulting kinematic safety guarantees can be transferred to full-order humanoid dynamics under unknown disturbances. Simulation and real-robot experiments demonstrate that the proposed framework improves safety margins under model mismatch and reliably enforces multiple safety constraints in real time during locomotion, teleoperation, and single-leg balancing with hand control. Project website: https://kwlee365.github.io/SafeWBC-Website/

Abstract

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