Learning Holistic Whole-Body Loco-Manipulation with a Bipedal Mobile Manipulator
研究通过强化学习训练的全身控制器，使双足机器人能够协调腿部和手臂动作以完成操作任务，同时保持平衡。
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Representative Papers
Finite-Modal Realization and Operator-Norm Convergence of a Source-to-Observation Electromagnetic Scattering Green Operator
该研究通过使用向量球波函数实现和算子范数收敛方法，解决了多散射模型中源到观测算子的有限模式表示不准确的问题。
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Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training
To address the limitations of backpropagation through time (BPTT) and surrogate gradient methods for spiking neural network (SNN) training—including suboptimal accuracy, high temporal computational overhead, and excessive memory consumption—this paper proposes an enhanced self-distillation framework. Methodologically, it introduces: (1) a lightweight artificial neural network (ANN) branch that takes intermediate-layer spike rates of the SNN as input, enabling cross-modal knowledge transfer; (2) the first decomposition of teacher signals into reliable and unreliable components, where only the reliable component guides SNN optimization to improve convergence stability; and (3) the integration of rate-based backpropagation with self-distillation, eliminating temporal unrolling and gradient truncation. Evaluated on CIFAR-10/100, CIFAR10-DVS, and ImageNet, the method significantly reduces training complexity while surpassing state-of-the-art SNN training approaches in accuracy, validating the efficacy of this efficient co-optimization paradigm.
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Learning Holistic Whole-Body Loco-Manipulation with a Bipedal Mobile Manipulator
研究通过强化学习训练的全身控制器，使双足机器人能够协调腿部和手臂动作以完成操作任务，同时保持平衡。
0 citationsRead paper
Finite-Modal Realization and Operator-Norm Convergence of a Source-to-Observation Electromagnetic Scattering Green Operator
该研究通过使用向量球波函数实现和算子范数收敛方法，解决了多散射模型中源到观测算子的有限模式表示不准确的问题。
0 citationsRead paper
Enhanced Self-Distillation Framework for Efficient Spiking Neural Network Training
To address the limitations of backpropagation through time (BPTT) and surrogate gradient methods for spiking neural network (SNN) training—including suboptimal accuracy, high temporal computational overhead, and excessive memory consumption—this paper proposes an enhanced self-distillation framework. Methodologically, it introduces: (1) a lightweight artificial neural network (ANN) branch that takes intermediate-layer spike rates of the SNN as input, enabling cross-modal knowledge transfer; (2) the first decomposition of teacher signals into reliable and unreliable components, where only the reliable component guides SNN optimization to improve convergence stability; and (3) the integration of rate-based backpropagation with self-distillation, eliminating temporal unrolling and gradient truncation. Evaluated on CIFAR-10/100, CIFAR10-DVS, and ImageNet, the method significantly reduces training complexity while surpassing state-of-the-art SNN training approaches in accuracy, validating the efficacy of this efficient co-optimization paradigm.
0 citationsRead paper