Bilevel optimization with 3R recovery for neuro-symbolic long-horizon planning under logical constraints achieves 80% failure reduction and 57% time reduction on benchmarks.
Practice makes perfect: Planning to learn skill parameter policies,
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ActivePusher integrates residual-physics modeling with uncertainty-based active learning to improve data efficiency and planning success rates for nonprehensile manipulation in simulation and real-world settings.
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Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints
Bilevel optimization with 3R recovery for neuro-symbolic long-horizon planning under logical constraints achieves 80% failure reduction and 57% time reduction on benchmarks.
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ActivePusher: Active Learning and Planning with Residual Physics for Nonprehensile Manipulation
ActivePusher integrates residual-physics modeling with uncertainty-based active learning to improve data efficiency and planning success rates for nonprehensile manipulation in simulation and real-world settings.