Task and Motion Planning
18 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Task and Motion Planning models and implementationsMost implemented papers
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
To complete the task, an embodied agent must plan a sequence of actions to change the state of a large number of objects in the face of realistic physical constraints.
Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments
This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain.
JEDAI: A System for Skill-Aligned Explainable Robot Planning
This paper presents JEDAI, an AI system designed for outreach and educational efforts aimed at non-AI experts.
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim.
Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles
The algorithm performance is evaluated in simulated driving on a mixed-track with segments of different curvatures (right and left).
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation
We benchmark the performance of offline RL and IL algorithms on our assembly tasks and demonstrate the need to improve such algorithms to be able to solve our tasks in the real world, providing ample opportunities for future research.
LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
Through a series of simulations in a box-packing domain, we quantitatively demonstrate the effectiveness of LLM^3 in solving TAMP problems and the efficiency in selecting action parameters.
Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks
To rigorously evaluate the quality of our automatic labeling framework, we contribute an algorithm SIMILARITY to produce two novel metrics, temporal similarity and semantic similarity.
