89 citations · 345 across the 28 of their papers we have counts for
30 papers
EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning
Yichao Liang, Amber Li, Dat Nguyen +13
A robot should be able to learn through experiments how unfamiliar objects behave and interact, then plan with that knowledge. It need not start from scratch: physics engines suppl…
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
Bowen Li, Mayank Mishra, Y. Isabel Liu +7
Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforcement learning (RL) can learn re…
Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints
Qiwei Du, Zitong Zhan, Shaoshu Su +7
Task planning often suffers from severe efficiency bottlenecks when robots must reason over long-horizon action sequences under complex logical constraints, including object afford…
KinDER: A Physical Reasoning Benchmark for Robot Learning and Planning
Yixuan Huang, Bowen Li, Vaibhav Saxena +9
Robotic systems that interact with the physical world must reason about kinematic and dynamic constraints imposed by their own embodiment, their environment, and the task at hand.…
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…
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…
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy
Chen Wang, Kaiyi Ji, Junyi Geng +16
Data-driven methods such as reinforcement and imitation learning have achieved remarkable success in robot autonomy. However, their data-centric nature still hinders them from gene…
AirShot: Efficient Few-Shot Detection for Autonomous Exploration
Zihan Wang, Bowen Li, Chen Wang +1
Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples.…
PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving
Zhipeng Zhao, Bowen Li, Yi Du +2
Motion prediction is critical for autonomous off-road driving, however, it presents significantly more challenges than on-road driving because of the complex interaction between th…
VoxDet: Voxel Learning for Novel Instance Detection
Bowen Li, Jiashun Wang, Yaoyu Hu +2
Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representation…
AirLoc: Object-based Indoor Relocalization
Aryan, Bowen Li, Sebastian Scherer +2
Indoor relocalization is vital for both robotic tasks like autonomous exploration and civil applications such as navigation with a cell phone in a shopping mall. Some previous appr…
Tracker Meets Night: A Transformer Enhancer for UAV Tracking
Junjie Ye, Changhong Fu, Ziang Cao +3
Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of-the-arts can hardly carry on their superiority at night so far, thereb…
Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel Attention
Guangze Zheng, Changhong Fu, Junjie Ye +3
Although the manipulating of the unmanned aerial manipulator (UAM) has been widely studied, vision-based UAM approaching, which is crucial to the subsequent manipulating, generally…
PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
Bowen Li, Ziyuan Huang, Junjie Ye +4
Visual object tracking is essential to intelligent robots. Most existing approaches have ignored the online latency that can cause severe performance degradation during real-world…
PyPose: A Library for Robot Learning with Physics-based Optimization
Chen Wang, Dasong Gao, Kuan Xu +34
Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physic…
Robotic Interestingness via Human-Informed Few-Shot Object Detection
Seungchan Kim, Chen Wang, Bowen Li +1
Interestingness recognition is crucial for decision making in autonomous exploration for mobile robots. Previous methods proposed an unsupervised online learning approach that can…
Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis
Changhong Fu, Kunhan Lu, Guangze Zheng +4
Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of intelligent transportation sy…
AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
Bowen Li, Chen Wang, Pranay Reddy +2
Few-shot object detection has attracted increasing attention and rapidly progressed in recent years. However, the requirement of an exhaustive offline fine-tuning stage in existing…
HiFT: Hierarchical Feature Transformer for Aerial Tracking
Ziang Cao, Changhong Fu, Junjie Ye +2
Most existing Siamese-based tracking methods execute the classification and regression of the target object based on the similarity maps. However, they either employ a single map f…
SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking
Ziang Cao, Changhong Fu, Junjie Ye +2
Recently, the Siamese-based method has stood out from multitudinous tracking methods owing to its state-of-the-art (SOTA) performance. Nevertheless, due to various special challeng…