Task and motion planning using physics-based reasoning

Akbari, Aliakbar; Muhayyuddin,; Rosell, Jan

doi:10.1109/etfa.2015.7301471

Cited by 19 publications

(21 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They find the quantitative evaluations of human reasoning capabilities to be surprisingly similar to the results obtained from physics simulations. Other well-known terminology is temporal projection [9], physics-based reasoning [10] and physical reasoning [11] which deal with predicting real-world behavior using knowledge inferred from physics simulation. In terms of repetitively performing actions which improve environment manipulation strategies, robotic playing [12] is another related bio-inspired technique which compares trial-and-error behavior while accumulating environment knowledge with a children's way of exploring the world.…”

Section: Related Workmentioning

confidence: 99%

Self-supervised damage-avoiding manipulation strategy optimization via mental simulation

Doernbach

2019

Intel Serv Robotics

View full text Add to dashboard Cite

Everyday robotics are challenged to deal with autonomous product handling in applications like logistics or retail, possibly causing damage on the items during manipulation. Traditionally, most approaches try to minimize physical interaction with goods. However, this paper proposes to take into account any unintended object motion and to learn damage-minimizing manipulation strategies in a self-supervised way. The presented approach consists of a simulation-based planning method for an optimal manipulation sequence with respect to possible damage. The planned manipulation sequences are generalized to new, unseen scenes in the same application scenario using machine learning. This learned manipulation strategy is continuously refined in a self-supervised, simulationin-the-loop optimization cycle during load-free times of the system, commonly known as mental simulation. In parallel, the generated manipulation strategies can be deployed in near-real time in an anytime fashion. The approach is validated on an industrial container-unloading scenario and on a retail shelf-replenishment scenario.

show abstract

Section: Related Workmentioning

confidence: 99%

Self-supervised damage-avoiding manipulation strategy optimization via mental simulation

Doernbach

2019

Intel Serv Robotics

View full text Add to dashboard Cite

show abstract

“…In a similar direction, the ontological physics-based motion planning approach of Muhayyuddin et al (2015) performs a knowledge-based reasoning process to compute the way of manipulation for objects, thus reducing the planning search space. This knowledge-based framework can be used together with any sampling-based kinodynamic motion planner (such as RRT, KPIECE, SyCLoP), and ODE is set as state propagator.Moreover, this framework is also used for computing the motion plan and dynamic cost in integrated task and motion planning approaches such as Akbari et al (2015Akbari et al ( , 2016. The present proposal extends this approach for temporal goals described by an LTL formula.…”

Section: Physics-based Motion Planningmentioning

confidence: 99%

Physics-based Motion Planning with Temporal Logic Specifications

2017

Self Cite

View full text Add to dashboard Cite

The final version of this work is available at https://doi.org/10.1016/j.ifacol.2017.08.1578\ud Copyright of all papers belongs to IFAC and must be referenced if derivative journal papers are produced from the conference papers.One of the main foci of robotics is nowadays centered in providing a great degree of autonomy to robots. A fundamental step in this direction is to give them the ability to plan in discrete and continuous spaces to find the required motions to complete a complex task. In this line, some recent approaches describe tasks with Linear Temporal Logic (LTL) and reason on discrete actions to guide sampling-based motion planning, with the aim of finding dynamically-feasible motions that satisfy the temporal-logic task specifications. The present paper proposes an LTL planning approach enhanced with the use of ontologies to describe and reason about the task, on the one hand, and that includes physics-based motion planning to allow the purposeful manipulation of objects, on the other hand. The proposal has been implemented and is illustrated with didactic examples with a mobile robot in simple scenarios where some of the goals are occupied with objects that must be removed in order to fulfill the task.Peer ReviewedPostprint (author's final draft

show abstract

“…The actual cost can be computed by the physics-based motion planners as indicated by [20], however in this paper a unified cost is proposed that determines the cost of both transit and push/pull actions with respect to power consumed:…”

Section: Physics-based Motion Planningmentioning

confidence: 99%

“…This combination of ontological knowledge-based task planning and physics-based motion planning was further explored in [20] and [21]. The former approach first determined a number of potential plans and then computed the accumulated cost of each plan execution, in order to identify the most feasible plan, by calling the low-level physics-based motion planer.…”

Section: Introductionmentioning

confidence: 99%

Task planning using physics-based heuristics on manipulation actions

Akbari

Muhayyuddin

Rosell

2016

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA)

Self Cite

View full text Add to dashboard Cite

Abstract-In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physicsbased knowledge. The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.

show abstract

Task and motion planning using physics-based reasoning

Cited by 19 publications

References 11 publications

Self-supervised damage-avoiding manipulation strategy optimization via mental simulation

Self-supervised damage-avoiding manipulation strategy optimization via mental simulation

Physics-based Motion Planning with Temporal Logic Specifications

Task planning using physics-based heuristics on manipulation actions

Contact Info

Product

Resources

About