54 citations · 167 across the 12 of their papers we have counts for
14 papers
Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning
Jensen Gao, Siddharth Reddy, Glen Berseth +2
Adaptive interfaces can help users perform sequential decision-making tasks like robotic teleoperation given noisy, high-dimensional command signals (e.g., from a brain-computer in…
First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization
Siddharth Reddy, Sergey Levine, Anca D. Dragan
How can we train an assistive human-machine interface (e.g., an electromyography-based limb prosthesis) to translate a user's raw command signals into the actions of a robot or com…
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback
Jensen Gao, Siddharth Reddy, Glen Berseth +5
We aim to help users communicate their intent to machines using flexible, adaptive interfaces that translate arbitrary user input into desired actions. In this work, we focus on as…
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning
Sean Chen, Jensen Gao, Siddharth Reddy +3
Building assistive interfaces for controlling robots through arbitrary, high-dimensional, noisy inputs (e.g., webcam images of eye gaze) can be challenging, especially when it invo…
Pragmatic Image Compression for Human-in-the-Loop Decision-Making
Siddharth Reddy, Anca D. Dragan, Sergey Levine
Standard lossy image compression algorithms aim to preserve an image's appearance, while minimizing the number of bits needed to transmit it. However, the amount of information act…
Assisted Perception: Optimizing Observations to Communicate State
Siddharth Reddy, Sergey Levine, Anca D. Dragan
We aim to help users estimate the state of the world in tasks like robotic teleoperation and navigation with visual impairments, where users may have systematic biases that lead to…