Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Vela, Patricio A"'
This work addresses motion planning under uncertainty as a stochastic optimal control problem. The path distribution induced by the optimal controller corresponds to a posterior path distribution with a known form. To approximate this posterior, we f
Externí odkaz:
http://arxiv.org/abs/2411.03416
Autor:
Lin, Yunzhi, Zhao, Yipu, Chu, Fu-Jen, Chen, Xingyu, Wang, Weiyao, Tang, Hao, Vela, Patricio A., Feiszli, Matt, Liang, Kevin
To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mirror the diversity of real-world conditions. We additionally present a
Externí odkaz:
http://arxiv.org/abs/2410.06694
For assistive robots, one critical use case of SLAM is to support localization as they navigate through an environment completing tasks. Current SLAM benchmarks do not consider task-based deployments where repeatability (precision) is more critical t
Externí odkaz:
http://arxiv.org/abs/2409.16573
This study focuses on a layered, experience-based, multi-modal contact planning framework for agile quadrupedal locomotion over a constrained rebar environment. To this end, our hierarchical planner incorporates locomotion-specific modules into the h
Externí odkaz:
http://arxiv.org/abs/2311.08354
Autor:
Mahendran, Arun Niddish, Freeman, Caitlin, Chang, Alexander H., McDougall, Michael, Vela, Patricio A., Vikas, Vishesh
The adaptability of soft robots makes them ideal candidates to maneuver through unstructured environments. However, locomotion challenges arise due to complexities in modeling the body mechanics, actuation, and robot-environment dynamics. These facto
Externí odkaz:
http://arxiv.org/abs/2307.16385
Recent works have shown that sequence modeling can be effectively used to train reinforcement learning (RL) policies. However, the success of applying existing sequence models to planning, in which we wish to obtain a trajectory of actions to reach s
Externí odkaz:
http://arxiv.org/abs/2303.16189
This paper describes a hierarchical solution consisting of a multi-phase planner and a low-level safe controller to jointly solve the safe navigation problem in crowded, dynamic, and uncertain environments. The planner employs dynamic gap analysis an
Externí odkaz:
http://arxiv.org/abs/2303.14278
This paper extends the gap-based navigation technique in Potential Gap by guaranteeing safety for nonholonomic robots for all tiers of the local planner hierarchy, so called Safer Gap. The first tier generates a Bezier-based collision-free path throu
Externí odkaz:
http://arxiv.org/abs/2303.08243
Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance of detecting out-of-distribution (OOD) samples in the open world. In
Externí odkaz:
http://arxiv.org/abs/2303.07543
Home-assistant robots have been a long-standing research topic, and one of the biggest challenges is searching for required objects in housing environments. Previous object-goal navigation requires the robot to search for a target object category in
Externí odkaz:
http://arxiv.org/abs/2303.06228