SCAN-Planner is a spatial collision-aware local planning framework for quadruped robots, using a yaw-aware twin-cylinder footprint model and projected A* search to navigate narrow passages, cluttered indoor scenes, and large-scale 3D unstructured environments safely and efficiently.

This research proposes a novel multi-layered mathematical model using category theory and sheaf theory to describe the Software Component Ensemble Language (SCEL) for autonomous agent systems. The model treats components as points, ensembles as open sets, and distributed knowledge as sheaf data. Information sharing is modeled as 'gluing' local data, and system failures are quantified as topological obstructions via sheaf cohomology. This approach transforms verification of complex distributed systems into geometric analysis, providing structural insights for robust autonomic system design.

This paper presents an invariant extended Kalman filtering (InEKF) approach for real-time state estimation of humanoid robots on non-inertial ground using only onboard proprioceptive sensing. It estimates the robot's base position and velocity relative to the moving ground without direct ground motion measurements, using foot-mounted IMUs and kinematic constraints. Experiments on Digit robot show 96% faster convergence and 80% lower position errors on swaying ground, and average errors under 9 cm on rotating ground.

This paper introduces Playful Agentic Robot Learning, where an embodied coding agent uses self-directed play as a continual skill-learning stage. The proposed RATs framework proposes exploratory tasks, executes code policies, verifies progress, and distills successful executions into a persistent skill library. Experiments show significant improvements on LIBERO-PRO and MolmoSpaces, and transferable skills boost performance without fine-tuning.

The paper presents DiffusionVS, a diffusion-based visual servoing method that uses conditional denoising to generate camera velocity and online training for improved generalization. It achieves nearly 100% success in simulation and 93% in physical experiments, and can be integrated into existing visual servoing networks to boost performance.

3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI, combining geometric grounding with semantic and relational abstractions. They are relevant to robotics and computer vision problems including manipulation, navigation, task planning, and scene understanding. However, the field is fragmented across different communities with distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods and assess challenges for real-world deployment. This survey provides a unified and critical review of 3DSGs, focusing on open challenges and future directions. It formalizes 3DSGs under a common definition, analyzes modeling choices (node/edge attributes, hierarchical structure, dynamic scenes, affordance-aware extensions), rev…