We present SAGE, a system for open-vocabulary exploration in unknown 3D indoor environments that preserves coverage-oriented behavior while allowing semantic cues to reprioritize frontier selection. In simulations, SAGE outperforms baselines in object discovery and achieves 13.7x speedup over FTU. Real-world drone flights confirm its effectiveness.

Researchers propose π0-EqM, which replaces the flow-matching expert in π0 with an Equilibrium Matching decoder. Under a matched 300-step budget, it improves RoboTwin average success from 40.4% to 50.2% across 19 tasks and reveals a task-dependent non-monotonic relation between residual and success, termed the stationarity–executability gap.

This paper presents four progressively complex state estimators for legged robots that use foot-contact information to mitigate IMU drift, including a contact-aided invariant EKF, factor graph, fixed-lag smoother with contact-episode footholds, and a variant with evolving IMU bias. Implementations are available in GTSAM and ROS2.

This paper proposes UfM*, an efficient uncertainty estimation algorithm that uses a compact Gaussian mixture to measure multiview disagreement from motion, requiring only a single DNN inference per image. It reduces calibration error by 24-28% compared to ensembles while consuming only 3% energy and 0.02% memory, enabling real-time operation on resource-constrained robots.