QOED selects identifiable parameter directions via Fisher matrix eigenspace analysis and modifies exploration objectives to approximate ideal information gain under bounded nuisance assumptions, yielding 21-35% performance gains in robotic tasks.
Sampling-based system identification with active exploration for legged robot sim2real learning
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PRIME is a MAP optimization framework that refines onboard kinematics into dynamically consistent trajectories for legged robots while jointly estimating contact forces and inertial parameters using differentiable smoothed contact dynamics.
Controlled sim-to-sim experiments indicate that system identification with few rollouts outperforms widened domain randomization for closing sim-to-real gaps when dynamics are identifiable.
citing papers explorer
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Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Exploration
QOED selects identifiable parameter directions via Fisher matrix eigenspace analysis and modifies exploration objectives to approximate ideal information gain under bounded nuisance assumptions, yielding 21-35% performance gains in robotic tasks.
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PRIME: Physically-consistent Robotic Inertial and Motion Estimation for Legged and Humanoid Robots
PRIME is a MAP optimization framework that refines onboard kinematics into dynamically consistent trajectories for legged robots while jointly estimating contact forces and inertial parameters using differentiable smoothed contact dynamics.
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How Should a Simulation-to-Reality Transfer Budget Be Spent?
Controlled sim-to-sim experiments indicate that system identification with few rollouts outperforms widened domain randomization for closing sim-to-real gaps when dynamics are identifiable.