Robot Task Planning
12 papers with code • 2 benchmarks • 5 datasets
RLBench
PackIt
Synthetic Object Preference Adaptation Data
Taskography
Most implemented papers
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.
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.
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.
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.
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.
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.
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.
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
However, the plans produced naively by LLMs often cannot map precisely to admissible actions.
TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations.
