Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Reddy, Siddharth"'
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 interface). Recent advances in human-in-the-loop machine learning enable
Externí odkaz:
http://arxiv.org/abs/2309.03839
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 super
Externí odkaz:
http://arxiv.org/abs/2205.12381
Autor:
Gao, Jensen, Reddy, Siddharth, Berseth, Glen, Hardy, Nicholas, Natraj, Nikhilesh, Ganguly, Karunesh, Dragan, Anca D., Levine, Sergey
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 assistive typing applications in which a user cannot operate a keyboard,
Externí odkaz:
http://arxiv.org/abs/2203.02072
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 '
Externí odkaz:
http://arxiv.org/abs/2202.02465
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 actually needed by a user for downstream tasks -- e.g., deciding which pr
Externí odkaz:
http://arxiv.org/abs/2108.04219
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 suboptimal behavior: they might struggle to process observations from
Externí odkaz:
http://arxiv.org/abs/2008.02840
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 knows the rewards and unsafe states, but querying the user is expensiv
Externí odkaz:
http://arxiv.org/abs/1912.05652
Autonomous robots often encounter challenging situations where their control policies fail and an expert human operator must briefly intervene, e.g., through teleoperation. In settings where multiple robots act in separate environments, a single huma
Externí odkaz:
http://arxiv.org/abs/1910.02910
Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics. Supervised learning methods based on behavioral cloning (BC) suffer from distr
Externí odkaz:
http://arxiv.org/abs/1905.11108
Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a goal or reward function that best explains the actions of the observed a
Externí odkaz:
http://arxiv.org/abs/1805.08010