On the manipulation of articulated objects in human-robot cooperation scenarios

Articulated and flexible objects constitute a challenge for robot manipulation tasks, but are present in different real-world settings, including home and industrial environments. Approaches to the manipulation of such objects employ ad hoc strategies to sequence and perform actions on them depending on their physical or geometrical features, and on a priori target object configurations, whereas principled strategies to sequence basic manipulation actions for these objects have not been fully explored in the literature. In this paper, we propose a novel action planning and execution framework for the manipulation of articulated objects, which (i) employs action planning to sequence a set of actions leading to a target articulated object configuration, and (ii) allows humans to collaboratively carry out the plan with the robot, also interrupting its execution if needed. The framework adopts a formally defined representation of articulated objects. A link exists between the way articulated objects are perceived by the robot, how they are formally represented in the action planning and execution framework, and the complexity of the planning process. Results related to planning performance, and examples with a Baxter dualarm manipulator operating on articulated objects with humans are shown

A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems

Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices. Although there are a few approaches proposed to target at the challenging problem, they generally cannot scale to large-scale scheduling problems. In this paper, we formulate the hoist scheduling problem as a new temporal planning problem in the form of adapted PDDL, and propose a novel hierarchical temporal planning approach to efficiently solve the scheduling problem. Additionally, we provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem. We exhibit that the proposed approach is able to efficiently find solutions of high quality for large-scale real-life benchmark instances, with comparison to state-of-the-art baselines

Towards Efficient Anytime Computation and Execution of Decoupled Robustness Envelopes for Temporal Plans

One of the major limitations for the employment of model-based planning and scheduling in practical applications is the need of costly re-planning when an incongruence between the observed reality and the formal model is encountered during execution. Robustness Envelopes characterize the set of possible contingencies that a plan is able to address without re-planning, but their exact computation is extremely expensive; furthermore, general robustness envelopes are not amenable for efficient execution. In this paper, we present a novel, anytime algorithm to approximate Robustness Envelopes, making them scalable and executable. This is proven by an experimental analysis showing the efficiency of the algorithm, and by a concrete case study where the execution of robustness envelopes significantly reduces the number of re-plannings

DELPHIC: Practical DEL Planning via Possibilities (Extended Version)

Dynamic Epistemic Logic (DEL) provides a framework for epistemic planning that is capable of representing non-deterministic actions, partial observability, higher-order knowledge and both factual and epistemic change. The high expressivity of DEL challenges existing epistemic planners, which typically can handle only restricted fragments of the whole framework. The goal of this work is to push the envelop of practical DEL planning, ultimately aiming for epistemic planners to be able to deal with the full range of features offered by DEL. Towards this goal, we question the traditional semantics of DEL, defined in terms on Kripke models. In particular, we propose an equivalent semantics defined using, as main building block, so-called possibilities: non well-founded objects representing both factual properties of the world, and what agents consider to be possible. We call the resulting framework DELPHIC. We argue that DELPHIC indeed provides a more compact representation of epistemic states. To substantiate this claim, we implement both approaches in ASP and we set up an experimental evaluation to compare DELPHIC with the traditional, Kripke-based approach. The evaluation confirms that DELPHIC outperforms the traditional approach in space and time

ECHO: A hierarchical combination of classical and multi-agent epistemic planning problems

The continuous interest in Artificial Intelligence (AI) has brought, among other things, the development of several scenarios where multiple artificial entities interact with each other. As for all the other autonomous settings, these multi-agent systems require orchestration. This is, generally, achieved through techniques derived from the vast field of Automated Planning. Notably, arbitration in multi-agent domains is not only tasked with regulating how the agents act, but must also consider the interactions between the agents' information flows and must, therefore, reason on an epistemic level. This brings a substantial overhead that often diminishes the reasoning process's usability in real-world situations. To address this problem, we present ECHO, a hierarchical framework that embeds classical and multi-agent epistemic (epistemic, for brevity) planners in a single architecture. The idea is to combine (i) classical; and(ii) epistemic solvers to model efficiently the agents' interactions with the (i) 'physical world'; and(ii) information flows, respectively. In particular, the presented architecture starts by planning on the 'epistemic level', with a high level of abstraction, focusing only on the information flows. Then it refines the planning process, due to the classical planner, to fully characterize the interactions with the 'physical' world. To further optimize the solving process, we introduced the concept of macros in epistemic planning and enriched the 'classical' part of the domain with goal-networks. Finally, we evaluated our approach in an actual robotic environment showing that our architecture indeed reduces the overall computational time

Partial Order Temporal Plan Merging for Mobile Robot Tasks

For many mobile service robot applications, planning problems are based on deciding how and when to navigate to certain locations and execute certain tasks. Typically, many of these tasks are independent from one another, and the main objective is to obtain plans that efficiently take into account where these tasks can be executed and when execution is allowed. In this paper, we present an approach, based on merging of partial order plans with durative actions, that can quickly and effectively generate a plan for a set of independent goals. This plan exploits some of the synergies of the plans for each single task, such as common locations where certain actions should be executed. We evaluate our approach in benchmarking domains, comparing it with state-of-the-art planners and showing how it provides a good trade-off between the approach of sequencing the plans for each task (which is fast but produces poor results), and the approach of planning for a conjunction of all the goals (which is slow but produces good results)