Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. By reducing operator dependency and enhancing access to complex anatomical regions, robotic ultrasound can help improve workflow efficiency. Recent studies have demonstrated the potential of deep reinforcement learning (DRL) and imitation learning (IL) to enable more autonomous and intelligent robotic ultrasound navigation. However, the application of learning-based robotic ultrasound to computer-assisted surgical tasks, such as anatomy reconstruction and surgical guidance, remains largely unexplored. A key bottleneck for this is the lack of realistic and efficient simulation environments tailored to these tasks. In this work, we present SonoGym, a scalable simulation platform for robotic ultrasound, enabling parallel simulation across tens to hundreds of environments. Our framework supports realistic and real-time simulation of US data from CT-derived 3D models of the anatomy through both a physics-based and a Generative Adversarial Network (GAN) approach. Our framework enables the training of DRL and recent IL agents (vision transformers and diffusion policies) for relevant tasks in robotic orthopedic surgery by integrating common robotic platforms and orthopedic end effectors. We further incorporate submodular DRL---a recent method that handles history-dependent rewards---for anatomy reconstruction and safe reinforcement learning for surgery. Our results demonstrate successful policy learning across a range of scenarios, while also highlighting the limitations of current methods in clinically relevant environments. We believe our simulation can facilitate research in robot learning approaches for such challenging robotic surgery applications. Dataset, codes and videos are publicly available at https://sonogym.github.io/.

Robotic origami design allows creating meso-scale robotic systems and mechanisms not limited by degrees of freedom, miniaturization and assembly downsides of conventional transmission mechanisms. However, unlike the traditional rigid approaches, robotic origami application has been limited by the complex deformation and kinematics of the compliant joints and actuation based on active materials or conventional electric motors. To generalize their application at meso-scale requires a combination of the predictability of traditional rigid kinematics and the manufacturing flexibility of robotic origami. Here we present a study of conventional transmission mechanisms, including a slider-crank and cam-follower, made in quasi-2D form by selective machining and stacking of multiple layers of composite material with minimal assembly. Owing to a compliant design powered by low-profile piezoelectric motors, our 5.3 mm thick and lightweight mechanisms transmit rotational motion to translational movements in and out-of-plane. We develop analytic models that we validate in terms of force and motion output on our prototypes.