88 citations · 96 across the 6 of their papers we have counts for
5 papers · 1 filter
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control
Zhehui Huang, Sumeet Batra, Tao Chen +7
Reinforcement learning (RL) has shown promise in creating robust policies for robotics tasks. However, contemporary RL algorithms are data-hungry, often requiring billions of envir…
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation
Alexey Kamenev, Lirui Wang, Ollin Boer Bohan +6
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving. To this end, we present PredictionNet, a deep neural network (DNN) that predict…
Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning
Sumeet Batra, Zhehui Huang, Aleksei Petrenko +3
We demonstrate the possibility of learning drone swarm controllers that are zero-shot transferable to real quadrotors via large-scale multi-agent end-to-end reinforcement learning.…
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors
Artem Molchanov, Tao Chen, Wolfgang Hönig +3
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation. We use reinforcement learning to train policies in simulation that transfer remar…
Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations
Jonathan Tremblay, Thang To, Artem Molchanov +3
We present a system to infer and execute a human-readable program from a real-world demonstration. The system consists of a series of neural networks to perform perception, program…