ReSYNC learns recovery skills via RL then discovers and refines relational predicates to enable abstract planning that generalizes failure avoidance to unseen long-horizon tasks, outperforming baselines by over 50% in simulation and transferring to real robots.
Predicate invention from pixels via pretrained vision-language models
4 Pith papers cite this work. Polarity classification is still indexing.
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SPARK reaches 43.7% success on six LIBERO-PRO cells by LLM-generated typed behavior trees plus multi-prompt perception and recovery, more than doubling CaP-Agent0 and VLA baselines.
UniDomain extracts atomic PDDL domains from 12,393 robot videos to create a unified domain of 3137 operators and 2875 predicates, then retrieves and fuses relevant parts to enable zero-shot planning on unseen real-world tasks.
citing papers explorer
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UniDomain: Pretraining a Unified PDDL Domain from Real-World Demonstrations for Generalizable Robot Task Planning
UniDomain extracts atomic PDDL domains from 12,393 robot videos to create a unified domain of 3137 operators and 2875 predicates, then retrieves and fuses relevant parts to enable zero-shot planning on unseen real-world tasks.
- Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation