arXiv Computer Vision
Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees
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CoCoNav: Conformal Control for Safe Robot Navigation in Crowds
arXiv:2608. 07751v1 Announce Type: cross Abstract: Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors.
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Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
arXiv:2606. 02562v1 Announce Type: cross Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate.
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AdvMT: Adversarial Motion Transformer for Long-term Human Motion Prediction
arXiv:2401.05018v3 Announce Type: replace Abstract: Human motion prediction is a crucial capability for advanced robotic systems that interact with humans. In facilities with dynamic human-robot coll...
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HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark
arXiv:2608. 13555v1 Announce Type: cross Abstract: Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos.
arXiv Computer Vision
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HAP: A Hand-Driven Active Perception Framework for Egocentric Head Motion Prediction
The paper introduces HAP, a Hand-Driven Active Perception framework that predicts future six‑degree‑of‑freedom head motion in egocentric settings by conditioning on observed hand motion and inferred target context. HAP constructs a Predictive Target‑Centric Amodal Occlusion Graph to model current and potential occlusions among candidate objects, fuses this with hand and head motion history, and blends the learned trajectory with a constant‑velocity prior. Experiments on a public dataset and a newly released Bottle RGB‑D dataset demonstrate that HAP outperforms baseline methods in head‑motion prediction, highlighting the importance of hand‑driven intention and dynamic occlusion reasoning.
Hugging Face Trending Papers
Sep 15
ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation
Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can help identify ambiguous and unreliable predictions, enabling agents to make safer navigation decision...