FADA is a three-stage Planner-IDM method that achieves few-shot domain adaptation for humanoid control by distilling an oracle policy then finetuning only the IDM on short target-domain rollouts via supervised learning.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
FT-WBC introduces a decoupled policy architecture with a Fault Estimator and Posture Adaptation Module that converts unstable arm-driven posture requests into safe base commands under actuator failures in legged manipulators.
TAM is a policy-agnostic torque adaptation module trained in randomized simulation that improves zero-shot real-robot performance on dynamic manipulation tasks compared to system identification and RMA baselines.
citing papers explorer
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FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control
FADA is a three-stage Planner-IDM method that achieves few-shot domain adaptation for humanoid control by distilling an oracle policy then finetuning only the IDM on short target-domain rollouts via supervised learning.
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FT-WBC: Learning Fault-Tolerant Whole-Body Control for Legged Loco-Manipulation
FT-WBC introduces a decoupled policy architecture with a Fault Estimator and Posture Adaptation Module that converts unstable arm-driven posture requests into safe base commands under actuator failures in legged manipulators.
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TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation
TAM is a policy-agnostic torque adaptation module trained in randomized simulation that improves zero-shot real-robot performance on dynamic manipulation tasks compared to system identification and RMA baselines.