Zobrazeno 1 - 10
of 565
pro vyhledávání: '"Lawson, L A"'
Autor:
Park, Jung Yeon, Bhatt, Sujay, Zeng, Sihan, Wong, Lawson L. S., Koppel, Alec, Ganesh, Sumitra, Walters, Robin
Equivariant neural networks have shown great success in reinforcement learning, improving sample efficiency and generalization when there is symmetry in the task. However, in many problems, only approximate symmetry is present, which makes imposing e
Externí odkaz:
http://arxiv.org/abs/2411.04225
Autor:
Biza, Ondrej, Weng, Thomas, Sun, Lingfeng, Schmeckpeper, Karl, Kelestemur, Tarik, Ma, Yecheng Jason, Platt, Robert, van de Meent, Jan-Willem, Wong, Lawson L. S.
Reinforcement Learning (RL) has the potential to enable robots to learn from their own actions in the real world. Unfortunately, RL can be prohibitively expensive, in terms of on-robot runtime, due to inefficient exploration when learning from a spar
Externí odkaz:
http://arxiv.org/abs/2410.19989
Autor:
Pate, Seth, Wong, Lawson L. S.
We study the task of locating a user in a mapped indoor environment using natural language queries and images from the environment. Building on recent pretrained vision-language models, we learn a similarity score between text descriptions and images
Externí odkaz:
http://arxiv.org/abs/2410.03900
Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and physical systems. In particular, solutions to partial differential equations (PDEs) over manifolds depend critically on th
Externí odkaz:
http://arxiv.org/abs/2310.19589
Navigating in unseen environments is crucial for mobile robots. Enhancing them with the ability to follow instructions in natural language will further improve navigation efficiency in unseen cases. However, state-of-the-art (SOTA) vision-and-languag
Externí odkaz:
http://arxiv.org/abs/2310.10822
Learning for robot navigation presents a critical and challenging task. The scarcity and costliness of real-world datasets necessitate efficient learning approaches. In this letter, we exploit Euclidean symmetry in planning for 2D navigation, which o
Externí odkaz:
http://arxiv.org/abs/2309.13043
Autor:
Zhao, Linfeng, Howell, Owen, Park, Jung Yeon, Zhu, Xupeng, Walters, Robin, Wong, Lawson L. S.
In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws.These changes, which preserve distance, encompass isometric transformations
Externí odkaz:
http://arxiv.org/abs/2307.08226
Autor:
Biza, Ondrej, Thompson, Skye, Pagidi, Kishore Reddy, Kumar, Abhinav, van der Pol, Elise, Walters, Robin, Kipf, Thomas, van de Meent, Jan-Willem, Wong, Lawson L. S., Platt, Robert
Imitation learning of robot policies from few demonstrations is crucial in open-ended applications. We propose a new method, Interaction Warping, for learning SE(3) robotic manipulation policies from a single demonstration. We infer the 3D mesh of ea
Externí odkaz:
http://arxiv.org/abs/2306.12392
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture. These applications typically assume that the domain symmetry i
Externí odkaz:
http://arxiv.org/abs/2211.09231
Differentiable planning promises end-to-end differentiability and adaptivity. However, an issue prevents it from scaling up to larger-scale problems: they need to differentiate through forward iteration layers to compute gradients, which couples forw
Externí odkaz:
http://arxiv.org/abs/2210.13542