Zobrazeno 1 - 10
of 102
pro vyhledávání: '"HECKMAN, CHRISTOFFER"'
Online Diffusion-Based 3D Occupancy Prediction at the Frontier with Probabilistic Map Reconciliation
Autonomous navigation and exploration in unmapped environments remains a significant challenge in robotics due to the difficulty robots face in making commonsense inference of unobserved geometries. Recent advancements have demonstrated that generati
Externí odkaz:
http://arxiv.org/abs/2409.10681
Integrating language models into robotic exploration frameworks improves performance in unmapped environments by providing the ability to reason over semantic groundings, contextual cues, and temporal states. The proposed method employs large languag
Externí odkaz:
http://arxiv.org/abs/2406.17180
For autonomous ground vehicles (AGVs) deployed in suburban neighborhoods and other human-centric environments the problem of localization remains a fundamental challenge. There are well established methods for localization with GPS, lidar, and camera
Externí odkaz:
http://arxiv.org/abs/2405.00600
Autor:
Reed, Alec, Crowe, Brendan, Albin, Doncey, Achey, Lorin, Hayes, Bradley, Heckman, Christoffer
When exploring new areas, robotic systems generally exclusively plan and execute controls over geometry that has been directly measured. When entering space that was previously obstructed from view such as turning corners in hallways or entering new
Externí odkaz:
http://arxiv.org/abs/2403.11985
Degraded rangelands undergo continual shifts in the appearance and distribution of plant life. The nature of these changes however is subtle: between seasons seedlings sprout up and some flourish while others perish, meanwhile, over multiple seasons
Externí odkaz:
http://arxiv.org/abs/2312.07724
Millimeter Wave Radar is being adopted as a viable alternative to lidar and radar in adverse visually degraded conditions, such as the presence of fog and dust. However, this sensor modality suffers from severe sparsity and noise under nominal condit
Externí odkaz:
http://arxiv.org/abs/2310.13188
Recurrent neural network-based reinforcement learning systems are capable of complex motor control tasks such as locomotion and manipulation, however, much of their underlying mechanisms still remain difficult to interpret. Our aim is to leverage com
Externí odkaz:
http://arxiv.org/abs/2306.15793
Humans have the remarkable ability to navigate through unfamiliar environments by solely relying on our prior knowledge and descriptions of the environment. For robots to perform the same type of navigation, they need to be able to associate natural
Externí odkaz:
http://arxiv.org/abs/2306.09523
The nonlinear and stochastic relationship between noise covariance parameter values and state estimator performance makes optimal filter tuning a very challenging problem. Popular optimization-based tuning approaches can easily get trapped in local m
Externí odkaz:
http://arxiv.org/abs/2306.07225
Autor:
Reed, Alec, Heckman, Christoffer
In this paper, we provide an early look at our model for generating terrain that is occluded in the initial lidar scan or out of range of the sensor. As a proof of concept, we show that a transformer based framework is able to be overfit to predict t
Externí odkaz:
http://arxiv.org/abs/2306.07160