Rethinking 3D Shape Generation: Diffusion over Superquadrics

Diffusion models have advanced 3D shape generation, yet most methods still denoise in high-cardinality spaces (e.g., voxel/SDF grids, meshes, or point clouds), which is computationally and memory intensive and makes it difficult to scale in terms of both higher resolution and stronger controllability. We rethink the diffusion representation and propose to move diffusion from dense geometry to compact geometric primitives, representing each shape as a small set of superquadrics. Instead of operating on thousands to millions of geometric representation values, we leverage 7KB superquadric parameters (pose, size, and shape), drastically reducing diffusion-state dimensionality and per-step compute/memory. Our diffusion-over-superquadrics improves scalability by supporting broader capabilities (e.g., resolution-free point-cloud decoding, part-level editing, and constraint-based design) and achieving competitive surface-fidelity and distributional performance on standard benchmarks after point-cloud decoding, while enabling efficient generation within 0.6s per shape for most conditions.

Physical Human-Robot Interaction for Grasping in Augmented Reality via Rigid-Soft Robot Synergy

Hybrid rigid-soft robots combine the precision of rigid manipulators with the compliance and adaptability of soft arms, offering a promising approach for versatile grasping in unstructured environments. However, coordinating hybrid robots remains challenging, due to difficulties in modeling, perception, and cross-domain kinematics. In this work, we present a novel augmented reality (AR)-based physical human-robot interaction framework that enables direct teleoperation of a hybrid rigid-soft robot for simple reaching and grasping tasks. Using an AR headset, users can interact with a simulated model of the robotic system integrated into a general-purpose physics engine, which is superimposed on the real system, allowing simulated execution prior to real-world deployment. To ensure consistent behavior between the virtual and physical robots, we introduce a real-to-simulation parameter identification pipeline that leverages the inherent geometric properties of the soft robot, enabling accurate modeling of its static and dynamic behavior as well as the control system's response.

Learning Complicated Manipulation Skills via Deterministic Policy with Limited Demonstrations

Combined with demonstrations, deep reinforcement learning can efficiently develop policies for manipulators. However, it takes time to collect sufficient high-quality demonstrations in practice. And human demonstrations may be unsuitable for robots. The non-Markovian process and over-reliance on demonstrations are further challenges. For example, we found that RL agents are sensitive to demonstration quality in manipulation tasks and struggle to adapt to demonstrations directly from humans. Thus it is challenging to leverage low-quality and insufficient demonstrations to assist reinforcement learning in training better policies, and sometimes, limited demonstrations even lead to worse performance. We propose a new algorithm named TD3fG (TD3 learning from a generator) to solve these problems. It forms a smooth transition from learning from experts to learning from experience. This innovation can help agents extract prior knowledge while reducing the detrimental effects of the demonstrations. Our algorithm performs well in Adroit manipulator and MuJoCo tasks with limited demonstrations.