no code implementations • 7 Sep 2023 • Jensen Gao, Siddharth Reddy, Glen Berseth, Anca D. Dragan, Sergey Levine
We further evaluate on a simulated Sawyer pushing task with eye gaze control, and the Lunar Lander game with simulated user commands, and find that our method improves over baseline interfaces in these domains as well.
1 code implementation • 24 May 2022 • 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 computer when there is no prior mapping, we cannot ask the user for supervision in the form of action labels or reward feedback, and we do not have prior knowledge of the tasks the user is trying to accomplish?
no code implementations • 4 Mar 2022 • Jensen Gao, Siddharth Reddy, Glen Berseth, Nicholas Hardy, Nikhilesh Natraj, Karunesh Ganguly, Anca D. Dragan, Sergey Levine
In the typing domain, we leverage backspaces as feedback that the interface did not perform the desired action.
no code implementations • 5 Feb 2022 • Sean Chen, Jensen Gao, Siddharth Reddy, Glen Berseth, Anca D. Dragan, Sergey Levine
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 involves inferring the user's desired action in the absence of a natural 'default' interface.
1 code implementation • NeurIPS 2021 • 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.
no code implementations • ICLR 2021 • Jensen Gao, Siddharth Reddy, Glen Berseth, Nicholas Hardy, Nikhilesh Natraj, Karunesh Ganguly, Anca Dragan, Sergey Levine
In the typing domain, we leverage backspaces as implicit feedback that the interface did not perform the desired action.
1 code implementation • 6 Aug 2020 • Siddharth Reddy, Sergey Levine, Anca D. Dragan
We evaluate ASE in a user study with 12 participants who each perform four tasks: two tasks with known user biases -- bandwidth-limited image classification and a driving video game with observation delay -- and two with unknown biases that our method has to learn -- guided 2D navigation and a lunar lander teleoperation video game.
1 code implementation • ICML 2020 • Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike
To address this challenge, we propose an algorithm that safely and interactively learns a model of the user's reward function.
no code implementations • 22 Sep 2019 • Gokul Swamy, Siddharth Reddy, Sergey Levine, Anca D. Dragan
We learn a model of the user's preferences from observations of the user's choices in easy settings with a few robots, and use it in challenging settings with more robots to automatically identify which robot the user would most likely choose to control, if they were able to evaluate the states of all robots at all times.
5 code implementations • ICLR 2020 • Siddharth Reddy, Anca D. Dragan, Sergey Levine
Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation.
no code implementations • 27 Sep 2018 • Siddharth Reddy, Anca D. Dragan, Sergey Levine
Learning to imitate expert actions given demonstrations containing image observations is a difficult problem in robotic control.
1 code implementation • NeurIPS 2018 • 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.
1 code implementation • 6 Feb 2018 • Siddharth Reddy, Anca D. Dragan, Sergey Levine
In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal.
no code implementations • 23 Feb 2016 • Siddharth Reddy, Igor Labutov, Thorsten Joachims
In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments.
1 code implementation • 23 Feb 2016 • Siddharth Reddy, Igor Labutov, Siddhartha Banerjee, Thorsten Joachims
Second, we use this memory model to develop a stochastic model for spaced repetition systems.