13 citations · 27 across the 7 of their papers we have counts for
7 papers
What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Peide Huang, Xilun Zhang, Ziang Cao +6
Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inev…
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving
Jonathan Francis, Bingqing Chen, Weiran Yao +2
The feasibility of collecting a large amount of expert demonstrations has inspired growing research interests in learning-to-drive settings, where models learn by imitating the dri…
Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing
Jonathan Francis, Bingqing Chen, Siddha Ganju +10
We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary…
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection
Bingqing Chen, Luca Bondi, Samarjit Das
Anomaly detection has many important applications, such as monitoring industrial equipment. Despite recent advances in anomaly detection with deep-learning methods, it is unclear h…
COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility
Bingqing Chen, Jonathan Francis, Marco Pritoni +2
Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs,…
Learning a Distributed Control Scheme for Demand Flexibility in Thermostatically Controlled Loads
Bingqing Chen, Weiran Yao, Jonathan Francis +1
Demand flexibility is increasingly important for power grids, in light of growing penetration of renewable generation. Careful coordination of thermostatically controlled loads (TC…