SCORE constrains sim RL to the support of a real-data policy via flow steering, raising average success on eight dexterous tasks from 37.8% to 89.9%.
Asid: Active exploration for system identification in robotic manipulation
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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.
Multi-task pretraining of diffusion policies on diverse robot data produces more successful, robust, and data-efficient policies for dexterous manipulation than single-task baselines, with performance scaling with pretraining size and diversity.
Derives a contact-aware Fisher information measure to synthesize robot behaviors that maximize information-rich contacts for efficient object parameter learning.
IDEA elevates multi-agent policies to semantic actions with effect alignment and synchronization for improved sim-to-real robustness on navigation tasks.
citing papers explorer
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Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
SCORE constrains sim RL to the support of a real-data policy via flow steering, raising average success on eight dexterous tasks from 37.8% to 89.9%.
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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.
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A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
Multi-task pretraining of diffusion policies on diverse robot data produces more successful, robust, and data-efficient policies for dexterous manipulation than single-task baselines, with performance scaling with pretraining size and diversity.
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Behavior Synthesis via Contact-Aware Fisher Information Maximization
Derives a contact-aware Fisher information measure to synthesize robot behaviors that maximize information-rich contacts for efficient object parameter learning.
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IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control
IDEA elevates multi-agent policies to semantic actions with effect alignment and synchronization for improved sim-to-real robustness on navigation tasks.