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pro vyhledávání: '"LaGrassa, Alex"'
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{model precondition}. Empirical real-world trajectory data is valuable
Externí odkaz:
http://arxiv.org/abs/2401.04007
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 can fail when the target dataset contains transitions where the dynami
Externí odkaz:
http://arxiv.org/abs/2209.14261
Autor:
LaGrassa, Alex, Kroemer, Oliver
Publikováno v:
Proceedings of the 5th Conference on Robot Learning, PMLR 164 (2022) 491-500
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. Meanwhile, physics simulators are effective at modeling complex interactio
Externí odkaz:
http://arxiv.org/abs/2206.05573
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 situation, in which objects are described only by their properties, genera
Externí odkaz:
http://arxiv.org/abs/2112.11199
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 structure of skills and tasks, such as subgoal skills, shared skill imp
Externí odkaz:
http://arxiv.org/abs/2109.08771
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 a corner. Such tasks often involve switching dynamics as the robot mak
Externí odkaz:
http://arxiv.org/abs/2103.14256
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 subtasks can be achieved by task-axis controllers defined relative to t
Externí odkaz:
http://arxiv.org/abs/2011.04627
Publikováno v:
International Conference on Intelligent Robots and Systems (IROS). 9441-9448. Sept. 2020
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 be reliable. Meanwhile, learning is useful for adaptation, but can req
Externí odkaz:
http://arxiv.org/abs/2009.13732