Zirui Chen

Zhongyuan Kong, Lei Li, Erwin Ang Tien Yew et al.

This paper presents the design, modeling, and control of a dual-bearing magnetorheological grease (MRG) clutch for wearable haptic feedback. Compared with conventional MR fluid devices, the proposed clutch avoids leakage-related reliability degradation while achieving high torque density in a compact structure. To provide physical insight into the torque-generation mechanism, a physics-inspired interpretive model is introduced to capture the dominant relationship among excitation current, magnetic-field evolution in the bearing gaps, and clutch locking torque. To mitigate the undesirable ``sticky'' sensation caused by passive bidirectional braking, an intention-based control strategy with active demagnetization is further developed to enable smoother release during human--robot interaction. Experimental characterization shows that the proposed clutch achieves a maximum locking torque of 43.42\,N$\cdot$m at 1.3\,A and a torque-to-mass ratio of 96.5\,N$\cdot$m/kg. Bench tests, replay validation, teleoperation experiments, and user studies indicate that the proposed approach accelerates clutch release and improves perceived release transparency and contact-to-release smoothness, while maintaining effective multi-level kinesthetic rendering.

Zirui Chen, Xin Wang, Zhao Li et al.

Recent advances in knowledge representation learning (KRL) highlight the urgent necessity to unify symbolic knowledge graphs (KGs) with language models (LMs) for richer semantic understanding. However, existing approaches typically prioritize either graph structure or textual semantics, leaving a gap: a unified framework that simultaneously captures global KG connectivity, nuanced linguistic context, and discriminative reasoning semantics. To bridge this gap, we introduce KG-BiLM, a bidirectional LM framework that fuses structural cues from KGs with the semantic expressiveness of generative transformers. KG-BiLM incorporates three key components: (i) Bidirectional Knowledge Attention, which removes the causal mask to enable full interaction among all tokens and entities; (ii) Knowledge-Masked Prediction, which encourages the model to leverage both local semantic contexts and global graph connectivity; and (iii) Contrastive Graph Semantic Aggregation, which preserves KG structure via contrastive alignment of sampled sub-graph representations. Extensive experiments on standard benchmarks demonstrate that KG-BiLM outperforms strong baselines in link prediction, especially on large-scale graphs with complex multi-hop relations - validating its effectiveness in unifying structural information and textual semantics.