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pro vyhledávání: '"Clark, Geoffrey"'
This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural network repair in a two-step supervised learning framework. It first learns
Externí odkaz:
http://arxiv.org/abs/2411.04408
Autor:
Calvert, Duncan, Penco, Luigi, Anderson, Dexton, Bialek, Tomasz, Chatterjee, Arghya, Mishra, Bhavyansh, Clark, Geoffrey, Bertrand, Sylvain, Griffin, Robert
Towards the role of humanoid robots as squad mates in urban operations and other domains, we identified doors as a major area lacking capability development. In this paper, we focus on the ability of humanoid robots to navigate and deal with doors. H
Externí odkaz:
http://arxiv.org/abs/2411.03532
This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble Kalman filter that employs stochastic neural networks to model the process
Externí odkaz:
http://arxiv.org/abs/2308.09870
Autor:
Majd, Keyvan, Clark, Geoffrey, Khandait, Tanmay, Zhou, Siyu, Sankaranarayanan, Sriram, Fainekos, Georgios, Amor, Heni Ben
Guaranteeing safety in human-centric applications is critical in robot learning as the learned policies may demonstrate unsafe behaviors in formerly unseen scenarios. We present a framework to locally repair an erroneous policy network to satisfy a s
Externí odkaz:
http://arxiv.org/abs/2303.06582
Autor:
Majd, Keyvan, Clark, Geoffrey, Khandait, Tanmay, Zhou, Siyu, Sankaranarayanan, Sriram, Fainekos, Georgios, Amor, Heni Ben
Publikováno v:
PMLR 205:2148-2158, 2023
Assistive robotic devices are a particularly promising field of application for neural networks (NN) due to the need for personalization and hard-to-model human-machine interaction dynamics. However, NN based estimators and controllers may produce po
Externí odkaz:
http://arxiv.org/abs/2303.04431
Autor:
Nutman, Emily, Clark, Geoffrey, Leclerc, Mathieu, Anenburg, Michael, Willsher, Joshua, Scorsini, Elisa, Gaffney, Dylan, Summerhayes, Glenn, Gibbs, Melissa, Huntley, Jillian, Wailu, Sabu, Zaro, James, Wright, Duncan
Publikováno v:
In Journal of Archaeological Science: Reports October 2024 58
We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanical impact on the human musculoskeletal system. First, we extend Intera
Externí odkaz:
http://arxiv.org/abs/2011.07005
Autor:
Clark, Geoffrey, Campbell, Joseph, Sorkhabadi, Seyed Mostafa Rezayat, Zhang, Wenlong, Amor, Heni Ben
We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e
Externí odkaz:
http://arxiv.org/abs/2005.13139