A unifying theme in the design of intelligent agents is to efficiently optimize a policy based on what prior knowledge of the problem is available and what actions can be taken to learn more about it. Bandits are a canonical instance of this task that has been intensely studied in the literature. Most methods, however, typically rely solely on an agent's experimentation in a single environment (or multiple closely related environments). In this paper, we relax this assumption and consider the design of bandit algorithms from a combination of batch data and qualitative assumptions about the relatedness across different environments, represented in the form of causal models. In particular, we show that it is possible to exploit invariances across environments, wherever they may occur in the underlying causal model, to consistently improve learning. The resulting bandit algorithm has a sub-linear regret bound with an explicit dependency on a term that captures how informative related environments are for the task at hand; and may have substantially lower regret than experimentation-only bandit instances.

Abstract--This paper presents a novel and sustainable approach for improving beam selection in 5G and beyond networks using transfer learning and Reinforcement Learning (RL). Traditional RL-based beam selection models require extensive training time and computational resources, particularly when deployed in diverse environments with varying propagation characteristics posing a major challenge for scalability and energy efficiency. T o address this, we propose modeling the environment as a point cloud, where each point represents the locations of gNodeBs (gNBs) and surrounding scatterers. By computing the Chamfer distance between point clouds, structurally similar environments can be efficiently identified, enabling the reuse of pre-trained models through transfer learning. This methodology leads to a 16 reduction in training time and computational overhead, directly contributing to energy efficiency. By minimizing the need for retraining in each new deployment, our approach significantly lowers power consumption and supports the development of green and sustainable Artificial Intelligence (AI) in wireless systems. Furthermore, it accelerates time-to-deployment, reduces carbon emissions associated with training, and enhances the viability of deploying AI-driven communication systems at the edge. Simulation results confirm that our approach maintains high performance while drastically cutting energy costs, demonstrating the potential of transfer learning to enable scalable, adaptive, and environmentally conscious RL-based beam selection strategies in dynamic and diverse propagation environments.

Amphibians adapt their morphologies and motions to accommodate movement in both terrestrial and aquatic environments. Inspired by these biological features, we present PuffyBot, an untethered shape morphing robot capable of changing its body morphology to navigate multiple environments. Our robot design leverages a scissor-lift mechanism driven by a linear actuator as its primary structure to achieve shape morphing. The transformation enables a volume change from 255.00 cm3 to 423.75 cm3, modulating the buoyant force to counteract a downward force of 3.237 N due to 330 g mass of the robot. A bell-crank linkage is integrated with the scissor-lift mechanism, which adjusts the servo-actuated limbs by 90 degrees, allowing a seamless transition between crawling and swimming modes. The robot is fully waterproof, using thermoplastic polyurethane (TPU) fabric to ensure functionality in aquatic environments. The robot can operate untethered for two hours with an onboard battery of 1000 mA h. Our experimental results demonstrate multi-environment locomotion, including crawling on the land, crawling on the underwater floor, swimming on the water surface, and bimodal buoyancy adjustment to submerge underwater or resurface. These findings show the potential of shape morphing to create versatile and energy efficient robotic platforms suitable for diverse environments.