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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…
Influence-Based Reward Modulation for Implicit Communication in Human-Robot Interaction
Haoyang Jiang, Elizabeth A. Croft, Michael G. Burke
Communication is essential for successful interaction. In human-robot interaction, implicit communication holds the potential to enhance robots' understanding of human needs, emoti…
A Probabilistic Model for Skill Acquisition with Switching Latent Feedback Controllers
Juyan Zhang, Dana Kulic, Michael Burke
Manipulation tasks often consist of subtasks, each representing a distinct skill. Mastering these skills is essential for robots, as it enhances their autonomy, efficiency, adaptab…
Rendering Stable Features Improves Sampling-Based Localisation with Neural Radiance Fields
Boxuan Zhang, Lindsay Kleeman, Michael Burke
Neural radiance fields (NeRFs) are a powerful tool for implicit scene representations, allowing for differentiable rendering and the ability to make predictions about unseen viewpo…