Runtime hazard monitors for autonomous naviga- tion are conventionally built from geometric quantities: predicted clearance, time to collision, and required deceleration. A model-predictive controller that enforces safety through explicit con- straints computes, as a by-product of every control step, a second information channel that such monitors ignore: the Karush-Kuhn-Tucker multipliers of its constrained optimization, which measure the marginal control effort spent to maintain safety against each obstacle. This paper evaluates whether a horizon-weighted sum of those multipliers, a dual stress signal, provides a hazard monitor complementary to the geometric warnings the same state already supports. We compare it against a battery of fifteen geometric detectors tuned to a matched false-alarm budget, on preregistered held-out crossing scenarios driven through a physics simulator. The stress alarm actionably flags 4.7 times as many collisions missed by the entire geometric battery as the geometric battery flags in return (85 versus 18); combined, the two channels warn of three quarters of the collisions for which braking remained feasible, against under half for the geometric battery alone.
Jamilé Chahine, Wenqi Cai, John Abanes et al.· 0 citations
Safe human-to-humanoid motion imitation is crucial for shared environments, where direct motion retargeting may induce self-collision or human–humanoid collision due to embodiment mismatch, kinematic limits, perception uncertainty, and human proximity. This paper presents an online vision-aided safe human-to-humanoid motion imitation framework that integrates skeleton-based upper-body pose estimation, joint-space retargeting, and capsule-based Control Barrier Function Quadratic Program (CBF-QP) safety filtering. Human skeletal observations are mapped to a reduced eight-degree-of-freedom (8-DoF) humanoid upper-body command, while the CBF-QP layer computes a safety-corrected target that minimally modifies the nominal imitation command subject to robot self-collision and human–humanoid collision constraints. The framework is evaluated via simulation and hardware experiments under representative self-collision and human–humanoid interaction episodes, complemented by a comparative benchmark against velocity damping and potential field baselines. Furthermore, this work introduces an evaluation protocol combining geometric safety, command deviation, and local-link similarity metrics to systematically characterize the safety–imitation trade-off governed by CBF parameters. The results demonstrate that, within the tested moderate-speed regime, the proposed framework substantially reduces geometric collision violations while balancing imitation fidelity with online computational feasibility, thereby providing a viable foundation for safe human-guided humanoid motion deployment.
Wenqi Cai, John Abanes, N. Evangeliou et al.· Big Data and Cognitive Compu...· 0 citations
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