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Hierarchical AI Agent architecture for general robot manipulation, compiling sparse demonstrations into reusable Skill Blocks with frozen foundation backbones and physical self-healing.
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Introduces a voxelized 3D Gaussian representation for dense visual SLAM on embedded vision platforms, combining redundancy pruning and attribute quantization for compact mapping and tracking.
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Presents a self-supervised multimodal iterative denoising framework that learns corruption-stage estimation and residual reversal from noisy data alone without clean ground truth.
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First to extend Vision-Language-Action (VLA) models to Vision-Sound-Language-Action (VSLA) for manipulation, coupling causal audio memory and flow matching for sound-centric control.
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Adapts multi-camera Structure-from-Motion to autonomous driving scenes, combining rigid multi-camera geometry, road-surface filtering, and multi-run spatiotemporal map aggregation.
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Coupling VLM cognitive reasoning with foundation models for single-image moving object segmentation without optical flow.
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A 4D multi-view generative world model for robot demonstration augmentation, editing spatiotemporal appearances while strictly preserving recorded physical actions for downstream VLA policies.
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Presents LNR, a coarse-to-fine robust multi-model fitting framework that screens minimal sample sets and refines hypothesis neighborhoods to overcome combinatorial explosion under multi-structure overlap.
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Proposes a geometry-conditioned diffusion paradigm for sample consensus, iteratively refining candidate minimal sets to achieve robust estimation under extreme outlier ratios.
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Reinforcement-learning-guided hypothesis sampling for consensus-based robust estimation under extreme outlier ratios.
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