MATCH trains hybrid position-force RL policies that achieve up to 10% higher success rates and 5x fewer breaks than pose-only policies in fragile peg-in-hole tasks under localization uncertainty, with strong sim-to-real results.
Factory: Fast Contact for Robotic Assembly [J]
6 Pith papers cite this work. Polarity classification is still indexing.
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Multimodal Diffusion Forcing trains a diffusion model on partially masked multimodal robot trajectories to learn temporal and cross-modal dependencies for forceful manipulation.
A wet-lab simulation benchmark and success-checked augmentation loop for turning 30 demonstrations per task into policy training data, reported to lift SmolVLA from 44% to 75% simulated success.
CoRMA modifies RMA by replacing raw parameter adaptation with inference of a 6D semantic contact context via a causal Transformer trained with semantic regression and force-regime contrastive loss, yielding higher real-world success than FORGE baselines on PegInsert, GearMesh, and NutThread under ta
SimDist pretrains world models in simulation and adapts them to real-world robots by updating only the latent dynamics model, enabling rapid improvement on contact-rich tasks where prior methods fail.
MimicGen creates over 50K robot demonstrations from roughly 200 human ones, allowing imitation learning to achieve strong performance on complex long-horizon tasks like assembly and coffee preparation.
citing papers explorer
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Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty
MATCH trains hybrid position-force RL policies that achieve up to 10% higher success rates and 5x fewer breaks than pose-only policies in fragile peg-in-hole tasks under localization uncertainty, with strong sim-to-real results.
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Multimodal Diffusion Forcing for Forceful Manipulation
Multimodal Diffusion Forcing trains a diffusion model on partially masked multimodal robot trajectories to learn temporal and cross-modal dependencies for forceful manipulation.
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Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
A wet-lab simulation benchmark and success-checked augmentation loop for turning 30 demonstrations per task into policy training data, reported to lift SmolVLA from 44% to 75% simulated success.
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CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
CoRMA modifies RMA by replacing raw parameter adaptation with inference of a 6D semantic contact context via a causal Transformer trained with semantic regression and force-regime contrastive loss, yielding higher real-world success than FORGE baselines on PegInsert, GearMesh, and NutThread under ta
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Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
SimDist pretrains world models in simulation and adapts them to real-world robots by updating only the latent dynamics model, enabling rapid improvement on contact-rich tasks where prior methods fail.
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MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
MimicGen creates over 50K robot demonstrations from roughly 200 human ones, allowing imitation learning to achieve strong performance on complex long-horizon tasks like assembly and coffee preparation.