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…
Learning Human Objectives by Evaluating Hypothetical Behavior
Siddharth Reddy, Anca D. Dragan, Sergey Levine +2
We seek to align agent behavior with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user…
Scaled Autonomy: Enabling Human Operators to Control Robot Fleets
Gokul Swamy, Siddharth Reddy, Sergey Levine +1
Autonomous robots often encounter challenging situations where their control policies fail and an expert human operator must briefly intervene, e.g., through teleoperation. In sett…
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards
Siddharth Reddy, Anca D. Dragan, Sergey Levine
Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics. Supervis…
Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior
Siddharth Reddy, Anca D. Dragan, Sergey Levine
Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a go…
Shared Autonomy via Deep Reinforcement Learning
Siddharth Reddy, Anca D. Dragan, Sergey Levine
In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the goal…
Latent Skill Embedding for Personalized Lesson Sequence Recommendation
Siddharth Reddy, Igor Labutov, Thorsten Joachims
Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Laten…
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
Siddharth Reddy, Igor Labutov, Siddhartha Banerjee +1
In the study of human learning, there is broad evidence that our ability to retain information improves with repeated exposure and decays with delay since last exposure. This plays…
Stochastic PDEs and Quantitative Finance: The Black-Scholes-Merton Model of Options Pricing and Riskless Trading
Brandon Kaplowitz, Siddharth G. Reddy
Differential equations can be used to construct predictive models of a diverse set of real-world phenomena like heat transfer, predator-prey interactions, and missile tracking. In…