MPTP: Motion-Planning-aware Task Planning for Navigation in Belief Space
We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environments. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In this paper, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our approach by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work.
Code (1)
Tasks
Motion PlanningNavigateTask PlanningSimilar Papers 제목 키워드 기반
When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?
We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we replace classical RL algorithms with an LLM? We explore this question…
Reinforcement LearningContinuous ControlTask-Motion Planning for Navigation in Belief Space
We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environment. Autonomous robots operating in real world complex scenarios require planning in the discrete (task) space and the c…
Motion PlanningNavigateGAIDE: Graph-based Attention Masking for Spatial- and Embodiment-aware Motion Planning
Sampling-based motion planning algorithms are widely used for motion planning of robotic manipulators, but they often struggle with sample inefficiency in high-dimensional configuration spaces due to their reliance on un…
Motion PlanningConnectivity-Aware Representations for Constrained Motion Planning via Multi-Scale Contrastive Learning
The objective of constrained motion planning is to connect start and goal configurations while satisfying task-specific constraints. Motion planning becomes inefficient or infeasible when the configurations lie in discon…
Contrastive LearningMotion PlanningMotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion Prior
This work presents MotionCom, a training-free motion-aware diffusion based image composition, enabling automatic and seamless integration of target objects into new scenes with dynamically coherent results without finetu…
Image GenerationLanguage ModelingLanguage Modelling