Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined output. Although counterfactual explainers that support modifying the graph by both adding and removing edges have recently emerged, there is still a lack of general and efficient methods, especially when considering the quality of the generated explanations. Moreover, the problem remains far from solved, as existing methods exhibit different strengths and weaknesses, often trading off between explanation size, coverage and quality. For this reason, it is important to identify where each method performs well and where it falls short, so as to guide future research in the field. Thus, our study compares six state-of-the-art (SOTA) models on a diverse set of real-world and synthetic datasets, covering both binary and multi-class graph and node classification tasks, and evaluates their performance using diverse quantitative and qualitative metrics.
Maria Myrto Villia, Filippos Gouidis, T. Patkos et al.· 0 citations
Non-prehensile manipulation of freely moving objects on a mobile base represents a significant challenge in underactuated robotics. This paper presents a sim-to-real reinforcement learning pipeline for a Unitree Go2 robot tasked with balancing a free-rolling ping-pong ball on a board mounted on its trunk while maintaining stable posture or tracking commanded velocities. Built on Legged Gym and the Genesis simulation, the proposed framework augments standard quadrupedal locomotion with ball-aware observations, task-specific reward design, and curriculum learning for progressively harder balancing and locomotion regimes. To improve transfer, the method incorporates domain randomization, camera-rate-compatible ball observations that mimic asynchronous visual feedback, and deployment-oriented safeguards such as smooth startup action blending. The learned policies are evaluated through a three-stage pipeline: large-scale training in Genesis, sim-to-sim validation in MuJoCo, and deployment on a physical Unitree Go2 using vision-estimated board-frame ball states. Experimental results show that the proposed framework can achieve both standing ball balance and ball-balancing locomotion on hardware, while additional comparisons between PPO and SAC highlight a trade-off between nominal task performance and disturbance robustness. These results suggest that reinforcement learning, when combined with transfer-aware observation design and deployment mechanisms, provides a practical approach for dynamic ball-balancing control on quadruped robots.
