Abstract--T ask and motion planning (T AMP) for robotics manipulation necessitates long-horizon reasoning involving versatile actions and skills. While deterministic actions can be crafted by sampling or optimizing with certain constraints, planning actions with uncertainty, i.e., probabilistic actions, remains a challenge for T AMP . On the contrary, Reinforcement Learning (RL) excels in acquiring versatile, yet short-horizon, manipulation skills that are robust with uncertainties. Besides the policy, a RL skill is defined with data-driven logical components that enable the skill to be deployed by symbolic planning. A plan refinement sub-routine is designed to further tackle the inevitable effect uncertainties. In the experiments, we compare our method with baseline hierarchical planning from both T AMP and RL fields and illustrate the strength of the method. The results show that by embedding RL skills, we extend the capability of T AMP to domains with probabilistic skills, and improve the planning efficiency compared to the previous methods. Reinforcement Learning (RL) empowers robots to acquire manipulation skills without human programming. However, prior works mostly tackle single-skill or short-term manipulation tasks, such as grasping [1] or peg insertion [2] or synergies between two actions [3]. The long-horizon manipulation planning remains a challenge in the RL field because of expanding state/action spaces and sparse rewards etc [4].

Measuring a qubit is a fundamental yet error prone operation in quantum computing. These errors can stem from various sources such as crosstalk, spontaneous state-transitions, and excitation caused by the readout pulse. In this work, we utilize an integrated approach to deploy neural networks (NN) on to field programmable gate arrays (FPGA). We demonstrate that it is practical to design and implement a fully connected neural network accelerator for frequency-multiplexed readout balancing computational complexity with low latency requirements without significant loss in accuracy. The neural network is implemented by quantization of weights, activation functions, and inputs. The hardware accelerator performs frequency-multiplexed readout of 5 superconducting qubits in less than 50 ns on RFSoC ZCU111 FPGA which is first of its kind in the literature. These modules can be implemented and integrated in existing Quantum control and readout platforms using a RFSoC ZCU111 ready for experimental deployment.