Hierarchical AI Agent architecture for general robot manipulation, compiling sparse demonstrations into reusable Skill Blocks with frozen foundation backbones and physical self-healing.
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.
A 4D multi-view generative world model for robot demonstration augmentation, editing spatiotemporal appearances while strictly preserving recorded physical actions for downstream VLA policies.
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.
Adapts multi-camera Structure-from-Motion to autonomous driving scenes, combining rigid multi-camera geometry, road-surface filtering, and multi-run spatiotemporal map aggregation.
Coupling VLM cognitive reasoning with foundation models for single-image moving object segmentation without optical flow.
Presents a self-supervised multimodal iterative denoising framework that learns corruption-stage estimation and residual reversal from noisy data alone without clean ground truth.
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.
Proposes a geometry-conditioned diffusion paradigm for sample consensus, iteratively refining candidate minimal sets to achieve robust estimation under extreme outlier ratios.
Reinforcement-learning-guided hypothesis sampling for consensus-based robust estimation under extreme outlier ratios.
DTFI combines local LiDAR-camera feature fusion with IMM-UKF multiple-model filtering for 3D vehicle detection and tracking in highway driving.