Search Results for author: Siddharth Reddy

Found 14 papers, 8 papers with code

First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization

1 code implementation24 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?

ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning

no code implementations5 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.


Pragmatic Image Compression for Human-in-the-Loop Decision-Making

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.

Car Racing Decision Making +1

Assisted Perception: Optimizing Observations to Communicate State

1 code implementation6 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.

Image Classification

Learning Human Objectives by Evaluating Hypothetical Behavior

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.

Car Racing

Scaled Autonomy: Enabling Human Operators to Control Robot Fleets

no code implementations22 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.

Robot Navigation

SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

4 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.

Imitation Learning Q-Learning +1

What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning

no code implementations27 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.

Imitation Learning Q-Learning +1

Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior

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.


Shared Autonomy via Deep Reinforcement Learning

1 code implementation6 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.


Latent Skill Embedding for Personalized Lesson Sequence Recommendation

no code implementations23 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.

Collaborative Filtering Recommendation Systems

Unbounded Human Learning: Optimal Scheduling for Spaced Repetition

1 code implementation23 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.

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