AINeutralarXiv – CS AI · 17h ago6/10
🧠Researchers introduce MindClaw, a framework enabling robots to reason about human mental states in real-time and intervene with assistance only when genuinely helpful. The system extends Theory of Mind capabilities beyond offline recognition to closed-loop embodied assistance, outperforming direct vision-language model baselines by incorporating trigger-skill optimization for intervention calibration.
AIBullisharXiv – CS AI · 17h ago6/10
🧠Researchers propose a new method to certify the safety of belief-space safety filters (BeliefSF) in interactive robotics using conformal prediction, addressing the challenge of providing formal safety guarantees when robots deploy neural approximations and runtime inference. The approach reduces conservativeness in safety filtering while maintaining high-probability safety assurances, demonstrated through human-vehicle interaction simulations.
AIBearisharXiv – CS AI · 1d ago6/10
🧠Researchers introduce TouchSafeBench, a physics-grounded benchmark for evaluating how well vision-language models can detect robot collisions with humans and objects. Testing three frontier VLMs reveals critical safety gaps, with best performance below 50% accuracy, exposing that visual fluency in AI models does not guarantee physical safety accountability in real-world human-robot collaboration scenarios.
AINeutralarXiv – CS AI · May 116/10
🧠UNCOM is a zero-shot framework that enables robots to understand natural human commands in tabletop environments by integrating speech, gestures, and scene context without requiring task-specific training data. The system achieves 82.39% success rate on real-world interaction scenarios, demonstrating practical viability for general-purpose domestic robotics applications.
AIBullisharXiv – CS AI · Apr 156/10
🧠Researchers have developed a context-selective, multimodal memory system for social robots that mimics human cognitive processes by prioritizing emotionally salient and novel experiences. The system combines text and visual data to enable personalized, context-aware interactions with users, outperforming existing memory models and maintaining real-time performance.
AIBullisharXiv – CS AI · Mar 96/10
🧠Researchers developed XR-DT, an Extended Reality-enhanced Digital Twin framework that combines augmented, virtual, and mixed reality to improve human-robot interaction in shared workspaces. The system uses a novel Human-Aware Model Predictive Path Integral control model with ATLAS, a Transformer-based trajectory prediction system, to enable safer and more interpretable robot navigation around humans.
AIBullisharXiv – CS AI · Mar 36/106
🧠Researchers developed a Vision-Language Model capable of estimating 3D object positions from monocular RGB images for human-robot interaction. The model achieved a median accuracy of 13mm and can make acceptable predictions for robot interaction in 25% of cases, representing a five-fold improvement over baseline methods.
AIBullisharXiv – CS AI · Feb 276/103
🧠Researchers have developed SignVLA, the first sign language-driven Vision-Language-Action framework for human-robot interaction that directly translates sign gestures into robotic commands without requiring intermediate gloss annotations. The system currently focuses on real-time alphabet-level finger-spelling for robotic control and is designed to support future expansion to word and sentence-level understanding.
AIBullisharXiv – CS AI · Mar 115/10
🧠Researchers developed CMA-ES-IG, a new algorithm that helps robots learn user preferences more effectively by incorporating user experience considerations. The algorithm suggests perceptually distinct and informative robot behaviors for users to rank, showing improved scalability, computational efficiency, and user satisfaction compared to existing methods.
AINeutralarXiv – CS AI · Feb 274/107
🧠Researchers benchmarked small language models (SLMs) for leader-follower role classification in human-robot interaction, finding that fine-tuned Qwen2.5-0.5B achieves 86.66% accuracy with 22.2ms latency. The study demonstrates SLMs can effectively handle real-time role assignment for resource-constrained robots, though performance degrades with increased dialogue complexity.
