74 citations · 133 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…