74 citations · 133 across the 12 of their papers we have counts for
15 papers
Image-Guided Semantic Pseudo-LiDAR Point Generation for 3D Object Detection
Minseung Lee, Seokha Moon, Seung Joon Lee +2
In autonomous driving scenarios, accurate perception is becoming an even more critical task for safe navigation. While LiDAR provides precise spatial data, its inherent sparsity ma…
Achieving Human Level Competitive Robot Table Tennis
David B. D'Ambrosio, Saminda Abeyruwan, Laura Graesser +24
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first…
VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions
Seokha Moon, Hyun Woo, Hongbeen Park +6
Predicting future trajectories for other road agents is an essential task for autonomous vehicles. Established trajectory prediction methods primarily use agent tracks generated by…
UniGen: Unified Modeling of Initial Agent States and Trajectories for Generating Autonomous Driving Scenarios
Reza Mahjourian, Rongbing Mu, Valerii Likhosherstov +4
This paper introduces UniGen, a novel approach to generating new traffic scenarios for evaluating and improving autonomous driving software through simulation. Our approach models…
Robotic Table Tennis: A Case Study into a High Speed Learning System
David B. D'Ambrosio, Jonathan Abelian, Saminda Abeyruwan +32
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the abilit…
Instance Segmentation with Cross-Modal Consistency
Alex Zihao Zhu, Vincent Casser, Reza Mahjourian +2
Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance seg…
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
Jinkyu Kim, Reza Mahjourian, Scott Ettinger +4
We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy.…
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
Reza Mahjourian, Jinkyu Kim, Yuning Chai +3
We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving. Our representation is a spatio-temporal g…
Identifying Driver Interactions via Conditional Behavior Prediction
Ekaterina Tolstaya, Reza Mahjourian, Carlton Downey +3
Interactive driving scenarios, such as lane changes, merges and unprotected turns, are some of the most challenging situations for autonomous driving. Planning in interactive scena…
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
Vincent Casser, Soeren Pirk, Reza Mahjourian +1
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically, we model the motion of…
Future Segmentation Using 3D Structure
Suhani Vora, Reza Mahjourian, Soeren Pirk +1
Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time vis…
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Vincent Casser, Soeren Pirk, Reza Mahjourian +1
Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and…
Hierarchical Policy Design for Sample-Efficient Learning of Robot Table Tennis Through Self-Play
Reza Mahjourian, Risto Miikkulainen, Nevena Lazic +2
Training robots with physical bodies requires developing new methods and action representations that allow the learning agents to explore the space of policies efficiently. This wo…
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
Reza Mahjourian, Martin Wicke, Anelia Angelova
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video. Unsupervised learning removes the need for separate supervisory signals (depth o…
Geometry-Based Next Frame Prediction from Monocular Video
Reza Mahjourian, Martin Wicke, Anelia Angelova
We consider the problem of next frame prediction from video input. A recurrent convolutional neural network is trained to predict depth from monocular video input, which, along wit…