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
Sequential Planning in Large Partially Observable Environments guided by LLMs
Heuristic methods, like monte-carlo tree search, though effective for large state space, but struggle if action space is large.
Vision-Language Interpreter for Robot Task Planning
By generating PDs from language instruction and scene observation, we can drive symbolic planners in a language-guided framework.
REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system.
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks.
Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
Despite recent success in large language model (LLM) reasoning, LLMs struggle with hierarchical multi-step reasoning tasks like generating complex programs.
BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard Environment
columbia-ai-robotics/BusyBot
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We introduce BusyBoard, a toy-inspired robot learning environment that leverages a diverse set of articulated objects and inter-object functional relations to provide rich visual feedback for robot interactions.
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.
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.
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.
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.
