Accurate transformation estimation between camera space and robot space is essential. Traditional methods using markers for hand-eye calibration require offline image collection, limiting their suitability for online self-calibration. Recent learning-based robot pose estimation methods, while advancing online calibration, struggle with cross-robot generalization and require the robot to be fully visible. This work proposes a Foundation feature-driven online End-Effector Pose Estimation (FEEPE) algorithm, characterized by its training-free and cross end-effector generalization capabilities. Inspired by the zero-shot generalization capabilities of foundation models, FEEPE leverages pre-trained visual features to estimate 2D-3D correspondences derived from the CAD model and target image, enabling 6D pose estimation via the PnP algorithm. To resolve ambiguities from partial observations and symmetry, a multi-historical key frame enhanced pose optimization algorithm is introduced, utilizing temporal information for improved accuracy. Compared to traditional hand-eye calibration, FEEPE enables marker-free online calibration. Unlike robot pose estimation, it generalizes across robots and end-effectors in a training-free manner. Extensive experiments demonstrate its superior flexibility, generalization, and performance. Additional demon-strations are available at https://feepose.github.io/

Motion forecasting is an important component in autonomous driving systems. One of the most challenging problems in motion forecasting is interactive trajectory prediction, whose goal is to jointly forecasts the future trajectories of interacting agents. To this end, we present a large-scale interactive trajectory prediction dataset named INT2 for INTeractive trajectory prediction at INTersections. INT2 includes 612,000 scenes, each lasting 1 minute, containing up to 10,200 hours of data. The agent trajectories are auto-labeled by a high-performance offline temporal detection and fusion algorithm, whose quality is further inspected by human judges. Vectorized semantic maps and traffic light information are also included in INT2. Additionally, the dataset poses an interesting domain mismatch challenge. For each intersection, we treat rush-hour and non-rush-hour segments as different domains. We benchmark the best open-sourced interactive trajectory prediction method on INT2 and Waymo Open Motion, under in-domain and cross-domain settings. The dataset, code and models are publicly available at https://github.com/AIRDISCOVER/INT2.