89 citations · 345 across the 28 of their papers we have counts for
5 papers · 1 filter
Unifying Deep Predicate Invention with Pre-trained Foundation Models
Qianwei Wang, Bowen Li, Zhanpeng Luo +6
Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into discrete predicates that capture…
SLAP: Shortcut Learning for Abstract Planning
Y. Isabel Liu, Bowen Li, Benjamin Eysenbach +1
Long-horizon decision-making with sparse rewards and continuous states and actions remains a fundamental challenge in AI and robotics. Task and motion planning (TAMP) is a model-ba…
Fast Task Planning with Neuro-Symbolic Relaxation
Qiwei Du, Bowen Li, Yi Du +5
Real-world task planning requires long-horizon reasoning over large sets of objects with complex relationships and attributes, leading to a combinatorial explosion for classical sy…
Bilevel Learning for Bilevel Planning
Bowen Li, Tom Silver, Sebastian Scherer +1
A robot that learns from demonstrations should not just imitate what it sees -- it should understand the high-level concepts that are being demonstrated and generalize them to new…
VL-Nav: Neuro-Symbolic Reasoning-based Vision-Language Navigation
Yi Du, Taimeng Fu, Zhipeng Zhao +6
Navigating unseen, large-scale environments based on complex and abstract human instructions remains a formidable challenge for autonomous mobile robots. Addressing this requires r…