A continuum-mechanics-informed discretization embeds tendon-driven continuum robots in MuJoCo, enabling zero-shot sim-to-real transfer of imitation learning policies for contact-rich manipulation on a 3-segment physical TDCR.
IndustReal: Transferring Contact- Rich Assembly Tasks from Simulation to Reality
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
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cs.RO 5years
2026 5verdicts
UNVERDICTED 5representative citing papers
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.
AnnotateAnything converts passive 3D assets into manipulation-ready assets by combining vision-language reasoning for semantics with parallel physics pipelines for executable action annotations such as grasps and articulations.
MiTaS fuses multi-resolution tactile data from GelSight and Evetac sensors with vision using modality-specific stems and transformer fusion to condition flow-matching policies, reporting 80% average success on five contact-rich tasks versus 31-54% baselines.
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
citing papers explorer
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Do Rigid-Body Simulators Dream of Soft Robots? Learning Contact-Rich Manipulation for Tendon-Driven Continuum Robots
A continuum-mechanics-informed discretization embeds tendon-driven continuum robots in MuJoCo, enabling zero-shot sim-to-real transfer of imitation learning policies for contact-rich manipulation on a 3-segment physical TDCR.
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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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AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation
AnnotateAnything converts passive 3D assets into manipulation-ready assets by combining vision-language reasoning for semantics with parallel physics pipelines for executable action annotations such as grasps and articulations.
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Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
MiTaS fuses multi-resolution tactile data from GelSight and Evetac sensors with vision using modality-specific stems and transformer fusion to condition flow-matching policies, reporting 80% average success on five contact-rich tasks versus 31-54% baselines.
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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