RLBench is an ambitious large-scale benchmark and learning environment designed to facilitate research in a number of vision-guided manipulation research areas, including: reinforcement learning, imitation learning, multi-task learning, geometric computer vision, and in particular, few-shot learning.
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The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents. This ability is referred to as geometric planning. Recently, many interactive environments have been proposed to evaluate intelligent agents on various skills, however, none of them cater to the needs of geometric planning. PackIt is a virtual environment to evaluate and potentially learn the ability to do geometric planning, where an agent needs to take a sequence of actions to pack a set of objects into a box with limited space.
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The dataset concerns toy tasks that a human should teach to a robot. The number of task repetitions is limited in the dataset since the human should demonstrate the task to the robot only a few times.
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This dataset involves a 2D or 3D agent moving from a start to goal pose while interacting with nearby objects. These objects can influence position of the agent via attraction or repulsion forces as well as influence orientation via attraction to object's orientation. This dataset can be used to pre-train general policy behavior, which can be later fine-tuned quickly for a person's specific preferences. Example use-cases include: - self-driving cars maintaining distance from other cars - robot pick-and-place tasks with intermediate subtasks (ie: scanning factory items before dropping them off)
PDDL dataset of Rearrangement tasks in large-scale 3D scene graphs.
The SheetCopilot dataset contains 28 evaluation workbooks and 221 spreadsheet manipulation tasks that are applied to these workbooks. These tasks involve diverse atomic actions related to six task categories (i.e. Entry and manipulation, Formatting, Management, Charts, Pivot Table, and Formula).
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