Robot Task Planning
17 papers with code • 3 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Robot Task Planning
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Datasets
RLBench
PackIt
Synthetic Object Preference Adaptation Data
Taskography
SheetCopilot
Latest papers
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation.
Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation
stepjam/ARM
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Despite the success of reinforcement learning methods, they have yet to have their breakthrough moment when applied to a broad range of robotic manipulation tasks.
PackIt: A Virtual Environment for Geometric Planning
The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans
MIT-SPARK/Kimera
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Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
Task Planning with a Weighted Functional Object-Oriented Network
The paper also presents a task planning algorithm for the weighted FOON to allocate manipulation action load to the robot and human to achieve optimal performance while minimizing human effort.
The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances.
Visual Robot Task Planning
In this work, we propose a neural network architecture and associated planning algorithm that (1) learns a representation of the world useful for generating prospective futures after the application of high-level actions, (2) uses this generative model to simulate the result of sequences of high-level actions in a variety of environments, and (3) uses this same representation to evaluate these actions and perform tree search to find a sequence of high-level actions in a new environment.
