This paper presents a fully autonomous robotic system that performs sim-to-real transfer in complex longhorizon tasks involving navigation, recognition, grasping, and stacking in an environment with multiple obstacles. The key feature of the system is the ability to overcome typical sensing and actuation discrepancies during sim-to-real transfer and to achieve consistent performance without any algorithmic modifications. To accomplish this, a lightweight noise-resistant visual perception system and a nonlinearityrobust servo system are adopted. We conduct a series of tests in both simulated and realworld environments. The visual perception system achieves the speed of 11 ms per frame due to its lightweight nature, and the servo system achieves sub-centimeter accuracy with the proposed controller. Both exhibit high consistency during sim-to-real transfer. Benefiting from these, our robotic system took first place in the mineral searching task of the Robotic Sim2Real Challenge hosted at ICRA 2024.

Quadrotor swarm with advanced coordination capabilities has garnered widespread attention, yet efficient and reliable motion planning remains a challenge. This paper presents a hierarchical motion planning framework for quadrotor swarm autonomous navigation in unknown, cluttered scenes. Specifically, we take a step forward sampling of multiple neighbors in the lattice graph and generate a group of paths. The infeasible paths are excluded and the one with the minimum cost is chosen from the remaining. Taking it as a guiding path, a gradient-free trajectory optimization method based on model predictive path integral (MPPI) is developed to produce the execution trajectory. Compared to gradient-descent approaches, it is capable of dealing with non-continuous and non-convex constraints. Additionally, the proposed method is deployed on GPUs in parallel to enhance efficiency. Extensive simulations demonstrate the robustness and effectiveness of the proposed motion planning framework.

Quantum computing has been the most promising in addressing computational challenging problems beyond classical computers’ capabilities. Recent work focus on developing systemon-chip (SoC) for quantum control and readout, yet there are scarce literature discussing how the measurement blocks are developed in details. This paper presents a quantum readout processing accelerator that operates at cryogenic temperature to process measurement data directly to obtain the qubit state. The proposed accelerator includes a coordinate rotation digital computer (CORDIC) based direct digital frequency synthesizer (DDFS) and a qubit state decision unit. To enable compact processing accelerator, the error of the qubit state prediction is analyzed when choosing different bitwidths and number of CORDIC iterations, aiming to balance the accuracy and hardware cost. The proposed accelerator, implemented using a 28-nm CMOS, consumes 36mW of power at room temperature and 8.9mW at −196°C. The processing latency and total energy consumption when determining a qubit state are reduced by 6 and 3 orders of magnitude respectively, when compared to a general purpose processor.