Research

Pushing the boundaries of how machines perceive and interact with the world.

Robotics & Embodied Systems

This research area targets integrated robotic and embodied AI: full systems that combine perception, reasoning, and planning on physical platforms—UAV and field robots, search-and-track missions, and human-facing deployment. It emphasizes social and in-the-wild human–robot interaction, aerial and conservation robotics, neuro-symbolic mission-level autonomy, and robotics-oriented datasets (pose, activity, multimodal sensing).

Robotics & Embodied Systems research artwork

Community Impact

Datasets & Benchmarks

Dataset Benchmark

JRDB: JackRabbot Dataset and Benchmark

JRDB is one of VL4AI's flagship robotic perception benchmarks. It anchors later work on pose estimation, trajectory forecasting, social understanding, and reasoning in crowded human environments.

JRDB: JackRabbot Dataset and Benchmark
Dataset Benchmark

MOT20: A Benchmark for Multi-object Tracking in Crowded Scenes

MOT20 is the benchmark anchor for VL4AI's crowded-scene multi-object tracking work.

MOT20: A Benchmark for Multi-object Tracking in Crowded Scenes
Dataset Benchmark

Social Motion Forecasting Benchmark

The SoMoF benchmark grouped VL4AI's trajectory and pose forecasting work around a public evaluation target.

Social Motion Forecasting Benchmark
Dataset Benchmark

Completion3D: Stanford 3D Point Cloud Completion Benchmark

Completion3D is a benchmark reference for structural point-cloud completion work such as TopNet.

Completion3D: Stanford 3D Point Cloud Completion Benchmark
Dataset Benchmark

LISC: Leukocyte Images for Segmentation and Classification

The LISC database contains hematological images taken from peripheral blood of healthy subjects. The database was released to enable comparative evaluation of nucleus and cytoplasm segmentation methods as well as recognition of different white blood cells in hematological images.

Archived Dataset Link
LISC: Leukocyte Images for Segmentation and Classification