A reinforcement learning method lets legged robots jointly learn information-seeking actions and predict joint-level and global embodiment parameters using a history-augmented URMA model in simulation.
Identifiability and identification of inertial parameters using the underactuated base-link dynamics for legged multibody systems
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Active Embodiment Identification with Reinforcement Learning for Legged Robots
A reinforcement learning method lets legged robots jointly learn information-seeking actions and predict joint-level and global embodiment parameters using a history-augmented URMA model in simulation.