26 citations · 30 across the 7 of their papers we have counts for
7 papers
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…
Aug3D: Augmenting large scale outdoor datasets for Generalizable Novel View Synthesis
Aditya Rauniyar, Omar Alama, Silong Yong +2
Recent photorealistic Novel View Synthesis (NVS) advances have increasingly gained attention. However, these approaches remain constrained to small indoor scenes. While optimizatio…
LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
Bowen Li, Zhaoyu Li, Qiwei Du +10
Recent years have witnessed the rapid development of Neuro-Symbolic (NeSy) AI systems, which integrate symbolic reasoning into deep neural networks. However, most of the existing b…
MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions
Cherie Ho, Seungchan Kim, Brady Moon +6
Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on robots exploring structured indoor environments which are…
Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data
Cherie Ho, Jiaye Zou, Omar Alama +7
Top-down Bird's Eye View (BEV) maps are a popular representation for ground robot navigation due to their richness and flexibility for downstream tasks. While recent methods have s…
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints
Yu Quan Chong, Jiaoyang Li, Katia Sycara
The Multi-Agent Path Finding (MAPF) problem entails finding collision-free paths for a set of agents, guiding them from their start to goal locations. However, MAPF does not accoun…
Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Yafei Hu, Quanting Xie, Vidhi Jain +20
Building general-purpose robots that operate seamlessly in any environment, with any object, and utilizing various skills to complete diverse tasks has been a long-standing goal in…