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.
4
Pith papers citing it
fields
cs.RO 4representative citing papers
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.
An LLM generates PDDL planning domains, uses a symbolic planner to create problem-plan pairs, converts them into chain-of-thought explanations, and fine-tunes itself, improving planning success on synthetic benchmarks.
citing papers explorer
-
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
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.
-
Sequential Planning via Anchored Robotic Keypoints
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: 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
An LLM generates PDDL planning domains, uses a symbolic planner to create problem-plan pairs, converts them into chain-of-thought explanations, and fine-tunes itself, improving planning success on synthetic benchmarks.