S2P learns separate location and insertion primitives simultaneously via visual RL for peg-in-hole tasks, improving sample efficiency and success rates across polygon benchmarks in simulation and real-world tests.
Visual-Force- Tactile Fusion for Gentle Intricate Insertion Tasks,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
A multimodal RGB-depth fusion backbone with vision transformer, masked-token contrastive learning, and curriculum domain randomization outperforms baselines in simulation and enables zero-shot real-world robot manipulation.
citing papers explorer
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A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks
S2P learns separate location and insertion primitives simultaneously via visual RL for peg-in-hole tasks, improving sample efficiency and success rates across polygon benchmarks in simulation and real-world tests.
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Multimodal Fusion for Sim2real Transfer in Visual Reinforcement Learning
A multimodal RGB-depth fusion backbone with vision transformer, masked-token contrastive learning, and curriculum domain randomization outperforms baselines in simulation and enables zero-shot real-world robot manipulation.