Task and Motion Planning for Execution in the Real
- URL: http://arxiv.org/abs/2406.03641v2
- Date: Thu, 13 Jun 2024 16:05:27 GMT
- Title: Task and Motion Planning for Execution in the Real
- Authors: Tianyang Pan, Rahul Shome, Lydia E. Kavraki,
- Abstract summary: This work generates task and motion plans that include actions cannot be fully grounded at planning time.
Execution combines offline planned motions and online behaviors till reaching the task goal.
Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework.
Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise.
- Score: 24.01204729304763
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reasoning necessitates task domain models and enough information to ground actions to motion planning queries. Gaps in this knowledge often arise from sources like occlusion or imprecise modeling. This work generates task and motion plans that include actions cannot be fully grounded at planning time. During execution, such an action is handled by a provided human-designed or learned closed-loop behavior. Execution combines offline planned motions and online behaviors till reaching the task goal. Failures of behaviors are fed back as constraints to find new plans. Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework and compare against state-of-the-art. Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise. The experiment data is shared for researchers to simulate these settings. The work shows promise in expanding the applicable class of realistic partially grounded problems that robots can address.
Related papers
- Neural MP: A Generalist Neural Motion Planner [75.82675575009077]
We seek to do the same by applying data-driven learning at scale to the problem of motion planning.
Our approach builds a large number of complex scenes in simulation, collects expert data from a motion planner, then distills it into a reactive generalist policy.
We perform a thorough evaluation of our method on 64 motion planning tasks across four diverse environments.
arXiv Detail & Related papers (2024-09-09T17:59:45Z) - Towards Bridging the Gap between High-Level Reasoning and Execution on
Robots [2.6107298043931206]
When reasoning about actions, e.g., by means of task planning or agent programming with Golog, the robot's actions are typically modeled on an abstract level.
However, when executing such an action on a robot it can no longer be seen as a primitive.
In this thesis, we propose several approaches towards closing this gap.
arXiv Detail & Related papers (2023-12-30T12:26:12Z) - Unified Task and Motion Planning using Object-centric Abstractions of
Motion Constraints [56.283944756315066]
We propose an alternative TAMP approach that unifies task and motion planning into a single search.
Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
arXiv Detail & Related papers (2023-12-29T14:00:20Z) - Planning as In-Painting: A Diffusion-Based Embodied Task Planning
Framework for Environments under Uncertainty [56.30846158280031]
Task planning for embodied AI has been one of the most challenging problems.
We propose a task-agnostic method named 'planning as in-painting'
The proposed framework achieves promising performances in various embodied AI tasks.
arXiv Detail & Related papers (2023-12-02T10:07:17Z) - Skip-Plan: Procedure Planning in Instructional Videos via Condensed
Action Space Learning [85.84504287685884]
Skip-Plan is a condensed action space learning method for procedure planning in instructional videos.
By skipping uncertain nodes and edges in action chains, we transfer long and complex sequence functions into short but reliable ones.
Our model explores all sorts of reliable sub-relations within an action sequence in the condensed action space.
arXiv Detail & Related papers (2023-10-01T08:02:33Z) - AI planning in the imagination: High-level planning on learned abstract
search spaces [68.75684174531962]
We propose a new method, called PiZero, that gives an agent the ability to plan in an abstract search space that the agent learns during training.
We evaluate our method on multiple domains, including the traveling salesman problem, Sokoban, 2048, the facility location problem, and Pacman.
arXiv Detail & Related papers (2023-08-16T22:47:16Z) - Optimal task and motion planning and execution for human-robot
multi-agent systems in dynamic environments [54.39292848359306]
We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks.
The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task.
We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
arXiv Detail & Related papers (2023-03-27T01:50:45Z) - Learning to Search in Task and Motion Planning with Streams [20.003445874753233]
Task and motion planning problems in robotics combine symbolic planning over discrete task variables with motion optimization over continuous state and action variables.
We propose a geometrically informed symbolic planner that expands the set of objects and facts in a best-first manner.
We apply our algorithm on a 7DOF robotic arm in block-stacking manipulation tasks.
arXiv Detail & Related papers (2021-11-25T15:58:31Z) - Task Scoping: Generating Task-Specific Abstractions for Planning [19.411900372400183]
Planning to solve any specific task using an open-scope world model is computationally intractable.
We propose task scoping: a method that exploits knowledge of the initial condition, goal condition, and transition-dynamics structure of a task.
We prove that task scoping never deletes relevant factors or actions, characterize its computational complexity, and characterize the planning problems for which it is especially useful.
arXiv Detail & Related papers (2020-10-17T21:19:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.