Search Results for author:
Found 16 papers, 10 papers with code
Learning High-Speed Flight in the Wild
Indeed, the subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline.
Jointly Learning Identification and Control for Few-Shot Policy Adaptation
We evaluate our approach on several complex systems and tasks, and experimentally analyze the advantages over model-free and model-based methods in terms of performance and sample efficiency.
Training Graph Neural Networks with 1000 Layers
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
OpenBot: Turning Smartphones into Robots
We develop a software stack that allows smartphones to use this body for mobile operation and demonstrate that the system is sufficiently powerful to support advanced robotics workloads such as person following and real-time autonomous navigation in unstructured environments.
Deep Drone Acrobatics
In this paper, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.
Robotics
SGAS: Sequential Greedy Architecture Search
Architecture design has become a crucial component of successful deep learning.
Ranked #3 on
Node Classification
on PPI
DeepGCNs: Making GCNs Go as Deep as CNNs
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
Ranked #4 on
Node Classification
on PPI
Learning a Controller Fusion Network by Online Trajectory Filtering for Vision-based UAV Racing
A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert.
DeepGCNs: Can GCNs Go as Deep as CNNs?
Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3. 7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation.
SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications
In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks.
Driving Policy Transfer via Modularity and Abstraction
Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment.
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
In this work, we present TrackingNet, the first large-scale dataset and benchmark for object tracking in the wild.
OIL: Observational Imitation Learning
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images.
End-to-end Driving via Conditional Imitation Learning
However, driving policies trained via imitation learning cannot be controlled at test time.
Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications
We present a photo-realistic training and evaluation simulator (Sim4CV) with extensive applications across various fields of computer vision.
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation
Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years.
