ACORN: Adaptive Contrastive Optimization for Safe and Robust Fine-Grained Robotic Manipulation

Embodied AI research has traditionally emphasized performance metrics such as success rate and cumulative reward, overlooking critical robustness and safety considerations that emerge during real-world deployment. In actual environments, agents continuously encounter unpredicted situations and distribution shifts, causing seemingly reliable policies to experience catastrophic failures, particularly in manipulation tasks. To address this gap, we introduce four novel safety-centric metrics that quantify an agent's resilience to environmental perturbations. Building on these metrics, we present Adaptive Contrastive Optimization for Robust Manipulation (ACORN), a plug-and-play algorithm that enhances policy robustness without sacrificing performance. ACORN leverages contrastive learning to simultaneously align trajectories with expert demonstrations while diverging from potentially unsafe behaviors. Our approach efficiently generates informative negative samples through structured Gaussian noise injection, employing a double perturbation technique that maintains sample diversity while minimizing computational overhead. Comprehensive experiments across diverse manipulation environments validate ACORN's effectiveness, yielding improvements of up to 23% in safety metrics under disturbance compared to baseline methods. These findings underscore ACORN's significant potential for enabling reliable deployment of embodied agents in safety-critical real-world applications.

Identifying modulation formats using integrated clustering algorithm

Modulation format identification (MFI) is crucial in next-generation optical networks such as cognitive optical networks. An integrated-clustering-algorithm-based MFI scheme in a coherent optical communication system is proposed herein. Numerical simulations are performed to test the performance of the scheme on the platform of a polarization domain multiplexing system at a symbol rate of 28 Gbaud. Simulations show that the MFI scheme can achieve an accuracy of 100% for five modulation formats considered in this study when the optical signal-to-noise ratio values are lower than the 7% forward error correction threshold. Tolerances to residual chromatic dispersion (CD) are discussed, and the results show that the proposed scheme can perform well as the residual CD changes over a wide range of values. Moreover, by selecting a number of key blocks, the proposed scheme is less complex than other clustering-based MFI schemes.