A fully Bayesian Hamiltonian Gaussian Process learns energy-consistent dynamics from input-output data by inferring hidden states and hyperparameters including damping coefficients.
Safelearninginrobotics:Fromlearning-based controltosafereinforcementlearning
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
MA-AC-MPC extends actor-critic MPC to multi-agent reinforcement learning and reports higher success rates than MLP baselines in pursuit-evasion simulation and hardware drone-rover landing.
Replicates SPARK humanoid safety filters and stress-tests them under crowding, noise, and delays, showing trade-offs in goal tracking versus collision reduction.
citing papers explorer
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Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes
A fully Bayesian Hamiltonian Gaussian Process learns energy-consistent dynamics from input-output data by inferring hidden states and hyperparameters including damping coefficients.
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Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies
MA-AC-MPC extends actor-critic MPC to multi-agent reinforcement learning and reports higher success rates than MLP baselines in pursuit-evasion simulation and hardware drone-rover landing.
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Adversarial Stress Testing of SPARK Humanoid Safety Filters
Replicates SPARK humanoid safety filters and stress-tests them under crowding, noise, and delays, showing trade-offs in goal tracking versus collision reduction.