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Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES
Functional Electrical Stimulation (FES) is a technique to evoke muscle contraction through low-energy electrical signals.
The role of haptic communication in dyadic collaborative object manipulation tasks
The task requires that the two sides coordinate with each other, in real-time, to balance the ball at the target.
The Response Shift Paradigm to Quantify Human Trust in AI Recommendations
Explainability, interpretability and how much they affect human trust in AI systems are ultimately problems of human cognition as much as machine learning, yet the effectiveness of AI recommendations and the trust afforded by end-users are typically not evaluated quantitatively.
MIDAS: Deep learning human action intention prediction from natural eye movement patterns
Our evaluation shows that intention prediction is not a naive result of the data, but rather relies on non-linear temporal processing of gaze cues.
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation
However, an open challenge remains on how to restore motor abilities to human limbs through FES, as the problem of controlling the stimulation is unclear.
Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform
We have pioneered the Where-You-Look-Is Where-You-Go approach to controlling mobility platforms by decoding how the user looks at the environment to understand where they want to navigate their mobility device.
Non-invasive Cognitive-level Human Interfacing for the Robotic Restoration of Reaching & Grasping
We combine wearable eye tracking, the visual context of the environment and the structural grammar of human actions to create a cognitive-level assistive robotic setup that enables the users in fulfilling activities of daily living, while conserving interpretability, and the agency of the user.
Real-World Human-Robot Collaborative Reinforcement Learning
We then use this setup to perform systematic experiments on human/agent behaviour and adaptation when co-learning a policy for the collaborative game.
Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions
We present a robotic setup for real-world testing and evaluation of human-robot and human-human collaborative learning.
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions
From a young age humans learn to use grammatical principles to hierarchically combine words into sentences.
Human Visual Attention Prediction Boosts Learning & Performance of Autonomous Driving Agents
Autonomous driving is a multi-task problem requiring a deep understanding of the visual environment.
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation
This is currently a challenge in deep learning systems.
FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping
Here we are demonstrating the off-loading of low-level control of assistive robotics and active orthotics, through automatic end-effector orientation control for grasping.
