Human-Robot Interaction Using Affective Cues
PulseAugur coverage of Human-Robot Interaction Using Affective Cues — every cluster mentioning Human-Robot Interaction Using Affective Cues across labs, papers, and developer communities, ranked by signal.
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New framework trains robots for embodied cognition using synthetic worlds
Researchers have developed a conceptual framework for training Vision-Language Models (VLMs) to enhance embodied cognition in robots, specifically focusing on Visual Perspective Taking (VPT). To facilitate this, they ge…
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VR and VLMs enhance robot situational awareness in hazardous settings
Researchers have developed a virtual reality (VR) framework to study how robots equipped with vision-language models (VLMs) can improve situational awareness in dangerous environments. The system allows a robot to explo…
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LLMs enable robots to synthesize actions from speech, gestures, and music
Researchers have developed a new framework that uses Large Language Models (LLMs) to enable robots to synthesize actions from multimodal human inputs. This system integrates speech recognition, gesture analysis, and mus…
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New Transformer Model Predicts Long-Term Human Motion with Occlusion Recovery
Researchers have developed a novel non-autoregressive transformer model for predicting human motion over extended periods. This model addresses limitations of existing autoregressive methods by focusing on both local po…
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Small Language Models Augment Human Reviewers in Tracking Robotics Research
Researchers have developed a systematic review pipeline to track the rapid growth in social-physical human-robot interaction (spHRI). This pipeline utilizes small language models (SLMs) to assist human reviewers in scre…
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Robot Pepper learns expressive gestures using ChatGPT and RLHF
Researchers have developed a novel method for generating natural and expressive gestures for the humanoid robot Pepper by integrating ChatGPT and Reinforcement Learning with Human Feedback (RLHF). Initial attempts using…
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New PATCH system enhances robot manipulation stability
Researchers have developed PATCH, a novel system for monitoring robot manipulation tasks in real-world environments. This action-chunk-conditioned latent patch innovation monitor aims to improve the robustness of learni…
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Robot interaction framework uses vision and speech for intent
Researchers have developed a new framework called EDITH that integrates verbal and nonverbal human signals for more natural human-robot interaction. This system captures first-person video, gaze, and speech from smart g…