A hierarchical multi-robot motion planner that refines workspace decompositions to enable scalable coordination through discrete search over smaller decoupled subproblems.
S ¸ucan, Mark Moll, and Lydia E
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TwinRL expands RL exploration via digital twin reconstruction and twin RL warm-up to guide real-world learning, reaching near-100% success with 20 minutes of on-robot time across four tasks.
OpenFrontier formulates robot navigation as sparse subgoal reaching via visual-language-grounded frontiers, achieving zero-shot performance without fine-tuning or dense semantic maps.
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.
citing papers explorer
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Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement
A hierarchical multi-robot motion planner that refines workspace decompositions to enable scalable coordination through discrete search over smaller decoupled subproblems.
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TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation
TwinRL expands RL exploration via digital twin reconstruction and twin RL warm-up to guide real-world learning, reaching near-100% success with 20 minutes of on-robot time across four tasks.
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OpenFrontier: General Navigation with Visual-Language Grounded Frontiers
OpenFrontier formulates robot navigation as sparse subgoal reaching via visual-language-grounded frontiers, achieving zero-shot performance without fine-tuning or dense semantic maps.
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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.
- Neural Configuration-Space Barriers for Manipulation Planning and Control