4 citations · 8 across the 8 of their papers we have counts for
9 papers
Planning-Query-Guided Model Generation for Model-Based Deformable Object Manipulation
Alex LaGrassa, Zixuan Huang, Dmitry Berenson +1
Efficient planning in high-dimensional spaces, such as those involving deformable objects, requires computationally tractable yet sufficiently expressive dynamics models. This pape…
Task-Oriented Active Learning of Model Preconditions for Inaccurate Dynamics Models
Alex LaGrassa, Moonyoung Lee, Oliver Kroemer
When planning with an inaccurate dynamics model, a practical strategy is to restrict planning to regions of state-action space where the model is accurate: also known as a \textit{…
Focused Adaptation of Dynamics Models for Deformable Object Manipulation
Peter Mitrano, Alex LaGrassa, Oliver Kroemer +1
In order to efficiently learn a dynamics model for a task in a new environment, one can adapt a model learned in a similar source environment. However, existing adaptation methods…
Learning Model Preconditions for Planning with Multiple Models
Alex LaGrassa, Oliver Kroemer
Different models can provide differing levels of fidelity when a robot is planning. Analytical models are often fast to evaluate but only work in limited ranges of conditions. Mean…
Specifying and achieving goals in open uncertain robot-manipulation domains
Leslie Pack Kaelbling, Alex LaGrassa, Tomás Lozano-Pérez
This paper describes an integrated solution to the problem of describing and interpreting goals for robots in open uncertain domains. Given a formal specification of a desired situ…
Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation
Jacky Liang, Mohit Sharma, Alex LaGrassa +3
Robots deployed in many real-world settings need to be able to acquire new skills and solve new tasks over time. Prior works on planning with skills often make assumptions on the s…
Learning Reactive and Predictive Differentiable Controllers for Switching Linear Dynamical Models
Saumya Saxena, Alex LaGrassa, Oliver Kroemer
Humans leverage the dynamics of the environment and their own bodies to accomplish challenging tasks such as grasping an object while walking past it or pushing off a wall to turn…
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation
Mohit Sharma, Jacky Liang, Jialiang Zhao +2
Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining contact with a table. Individual s…
Learning Skills to Patch Plans Based on Inaccurate Models
Alex LaGrassa, Steven Lee, Oliver Kroemer
Planners using accurate models can be effective for accomplishing manipulation tasks in the real world, but are typically highly specialized and require significant fine-tuning to…