10 citations · 16 across the 7 of their papers we have counts for
7 papers
Zero-Shot Constrained Motion Planning Transformers Using Learned Sampling Dictionaries
Jacob J. Johnson, Ahmed H. Qureshi, Michael C. Yip
Constrained robot motion planning is a ubiquitous need for robots interacting with everyday environments, but it is a notoriously difficult problem to solve. Many sampled points in…
Object-centric Representations for Interactive Online Learning with Non-Parametric Methods
Nikhil U. Shinde, Jacob Johnson, Sylvia Herbert +1
Large offline learning-based models have enabled robots to successfully interact with objects for a wide variety of tasks. However, these models rely on fairly consistent structure…
Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformers
Jacob J Johnson, Ahmed H Qureshi, Michael Yip
Motion planning is integral to robotics applications such as autonomous driving, surgical robots, and industrial manipulators. Existing planning methods lack scalability to higher-…
Chance-Constrained Motion Planning using Modeled Distance-to-Collision Functions
Jacob J. Johnson, Michael C. Yip
This paper introduces Chance Constrained Gaussian Process-Motion Planning (CCGP-MP), a motion planning algorithm for robotic systems under motion and state estimate uncertainties.…
Motion Planning Transformers: A Motion Planning Framework for Mobile Robots
Jacob J. Johnson, Uday S. Kalra, Ankit Bhatia +3
Fast and efficient sampling-based motion planning (SMP) is an integral component of many robotic systems, such as autonomous cars. A popular technique to improve the efficiency of…
Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots
Jacob J. Johnson, Linjun Li, Fei Liu +2
Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints be…
Composing Task-Agnostic Policies with Deep Reinforcement Learning
Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin +3
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines. To date, there has been plenty…