8 citations · 20 across the 6 of their papers we have counts for
6 papers
Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences
Emmanuel Fashae, Michael Burke, Leimin Tian +2
Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limi…
A Benchmark for Cycling Close Pass Detection from Video Streams
Mingjie Li, Ben Beck, Tharindu Rathnayake +5
Cycling is a healthy and sustainable mode of transport. However, interactions with motor vehicles remain a key barrier to increased cycling participation. The ability to detect pot…
Multi-Step First: A Lightweight Deep Reinforcement Learning Strategy for Robust Continuous Control with Partial Observability
Lingheng Meng, Rob Gorbet, Michael Burke +1
Deep Reinforcement Learning (DRL) has made considerable advances in simulated and physical robot control tasks, especially when problems admit a fully observed Markov Decision Proc…
Memory-based Deep Reinforcement Learning for POMDPs
Lingheng Meng, Rob Gorbet, Dana Kulić
A promising characteristic of Deep Reinforcement Learning (DRL) is its capability to learn optimal policy in an end-to-end manner without relying on feature engineering. However, m…
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning
Lingheng Meng, Rob Gorbet, Dana Kulić
Multi-step (also called n-step) methods in reinforcement learning (RL) have been shown to be more efficient than the 1-step method due to faster propagation of the reward signal, b…
Learning to Engage with Interactive Systems: A Field Study on Deep Reinforcement Learning in a Public Museum
Lingheng Meng, Daiwei Lin, Adam Francey +3
Physical agents that can autonomously generate engaging, life-like behaviour will lead to more responsive and interesting robots and other autonomous systems. Although many advance…