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GzDRL: Reproducible and Scalable Deep Reinforcement Learning with Gazebo
Amal Dev Haridevan, Junjie Kang, Jinjun Shan
We present GzDRL, a novel single-process reinforcement learning (RL) framework for Gazebo that overcomes longstanding bottlenecks in scalable, reproducible robotics experimentation…
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
Ehsan Ahmadi, Hunter Schofield, Behzad Khamidehi +5
Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, multi-agent interactions common i…
STaR: Scalable Task-Conditioned Retrieval for Long-Horizon Multimodal Robot Memory
Mingfeng Yuan, Hao Zhang, Mahan Mohammadi +3
Mobile robots are often deployed over long durations in diverse open, dynamic scenes, including indoor setting such as warehouses and manufacturing facilities, and outdoor settings…
Beyond Simulation: Benchmarking World Models for Planning and Causality in Autonomous Driving
Hunter Schofield, Mohammed Elmahgiubi, Kasra Rezaee +1
World models have become increasingly popular in acting as learned traffic simulators. Recent work has explored replacing traditional traffic simulators with world models for polic…
Vectorized Representation Dreamer (VRD): Dreaming-Assisted Multi-Agent Motion-Forecasting
Hunter Schofield, Hamidreza Mirkhani, Mohammed Elmahgiubi +2
For an autonomous vehicle to plan a path in its environment, it must be able to accurately forecast the trajectory of all dynamic objects in its proximity. While many traditional m…