The research in hierarchical planning has made considerable progress in the last few years. Many recent systems do not rely on hand-tailored advice anymore to find solutions, but are supposed to be domain-independent systems that come with sophisticated solving techniques. In principle, this development would make the comparison between systems easier (because the domains are not tailored to a single system anymore) and – much more important – also the integration into other systems, because the modeling process is less tedious (due to the lack of advice) and there is no (or less) commitment to a certain planning system the model is created for. However, these advantages are destroyed by the lack of a common input language and feature set supported by the different systems. In this paper, we propose an extension to PDDL, the description language used in non-hierarchical planning, to the needs of hierarchical planning systems.
Programming robots for general purpose applications is extremely challenging due to the great diversity of end-user tasks ranging from manufacturing environments to personal homes. Recent work has focused on enabling endusers to program robots using Programming by Demonstration. However, teaching robots new actions from scratch that can be reused for unseen tasks remains a difficult challenge and is generally left up to robotic experts. We propose iRoPro, an interactive Robot Programming framework that allows endusers to teach robots new actions from scratch and reuse them with a task planner. In this work we provide a system implementation on a two-armed Baxter robot that (i) allows simultaneous teaching of low-and high-level actions by demonstration, (ii) includes a user interface for action creation with condition inference and modification, and (iii) allows creating and solving previously unseen problems using a task planner for the robot to execute in real-time. We evaluate the generalisation power of the system on six benchmark tasks and show how taught actions can be easily reused for complex tasks. We further demonstrate its usability with a user study (N=21), where users completed eight tasks to teach the robot new actions that are reused with a task planner. The study demonstrates that users with any programming level and educational background can easily learn and use the system.
For social robots to be brought more into widespread use in the fields of companionship, care taking and domestic help, they must be capable of demonstrating social intelligence. In order to be acceptable, they must exhibit socio-communicative skills. Classic approaches to program HRI from observed human-human interactions fails to capture the subtlety of multimodal interactions as well as the key structural differences between robots and humans. The former arises due to a difficulty in quantifying and coding multimodal behaviours, while the latter due to a difference of the degrees of liberty between a robot and a human. However, the notion of reverse engineering from multimodal HRI traces to learn the underlying behavioral blueprint of the robot given multimodal traces seems an option worth exploring. With this spirit, the entire HRI can be seen as a sequence of exchanges of speech acts between the robot and human, each act treated as an action, bearing in mind that the entire sequence is goal-driven. Thus, this entire interaction can be treated as a sequence of actions propelling the interaction from its initial to goal state, also known as a plan in the domain of AI planning. In the same domain, this action sequence that stems from plan execution can be represented as a trace. AI techniques, such as machine learning, can be used to learn behavioral models (also known as symbolic action models in AI), intended to be reusable for AI planning, from the aforementioned multimodal traces. This article reviews recent machine learning techniques for learning planning action models which can be applied to the field of HRI with the intent of rendering robots as socio-communicative.
Many Artificial Intelligence techniques have been developed for intelligent and autonomous systems to act and make rational decisions based on perceptions of the world state. Among these techniques, HTN (Hierarchical Task Network) planning is one of the most used in practice. HTN planning is based on expressive languages allowing to specify complex expert knowledge for real world domains. At the same time, many preprocessing techniques for classical planning were proposed to speed up the search. One of these technique, named grounding, consists in enumerating and instantiating all the possible actions from the planning problem descriptions. This technique has proven its effectiveness. Therefore, combining the expressiveness of HTN planning with the efficiency of the grounding preprocessing techniques used in classical planning is a very challenging issue. In this paper, we propose a generic algorithm to ground the domain representation for HTN planning. We show experimentally that grounding process improves the performances of state of the art HTN planners on a range of planning problems from the International Planning Competition (IPC).
Arrangement of items on shelves in stores or warehouses is a tedious, repetitive task that can be feasible for robots to perform. The diversity of products that are available in stores and the different setups and preferences of each store makes pre-programming a robot for this task extremely challenging. Instead, our work argues for enabling end-users to customize the robot to their specific objects and setup at deployment time by programming it themselves. To that end, this paper contributes (i) a task representation for shelf arrangements based on a large dataset of grocery store shelf images, (ii) a method for inferring goal configurations from user inputs including demonstrations and direct parameter specifications, and (iii) a system implementation of the proposed approach that allows simultaneously learning task goals and actions. We evaluate our goal inference approach with ten different teaching strategies that combine alternative user inputs in different ways on the large dataset of grocery configurations, as well as with real human teachers through an online user study (N=32). We evaluate our full system implemented on a Fetch mobile manipulator on eight benchmark tasks that demonstrate endto-end programming and execution of shelf arrangement tasks.
Abstract-Cobots (collaborative robots) are revolutionising industries by allowing robots to work in close collaboration with humans. But many companies hesitate their adoption, due to the lack of programming experts. In this work, we evaluate a robot programming framework for non-expert users, that requires users to teach action models expressed in a symbolic planning language (PDDL). These action models would allow the robot to leverage modern automated planners to achieve any userdefined goal. We conducted qualitative user experiments with a Baxter robot to evaluate the non-expert user's understanding of the symbolic planning language and the usability of the framework. We showed that users with little to no programming experience can adopt the symbolic planning language, and use the framework. I. INTRODUCTIONRobots have been working in close collaboration with humans. Cobotic systems [1] have been adopted in several industries from the food-processing industry, to aeronautics, to the health industry. However, many companies remain robot resistant, as they lack programming experts to fully exploit the robots. Programming by Demonstration (PbD) allows non-experts to teach robots new skills by demonstrating a task, without writing any code [2]. It is an intuitive robot programming approach, with the goal to refine the robot's performance, by providing repetitive demonstrations. However, in existing PbD implementations the robot learns an action sequence [3], [4], rather than atomic actions that can be reused independently. Teaching full action sequences is often complicated and time-consuming, as the robot has to be demonstrated a new sequence, whenever the goal changes.In our previous work [5], we addressed the question "Can non-experts teach a robot atomic actions, which can be reused to automatically generate novel action sequences?" We proposed a framework that combines PbD and Automated Planning [6], where the robot learns action models by demonstration, and the problem of finding an action sequence is delegated to a planner. The robot programming process consists of steps: 1) the non-expert user demonstrates atomic actions to the robot, and teaches action models, expressed in a symbolic planning language (STRIPS [7]), 2) the robot uses these action models with an automated planner to generate solutions to user-defined goals, 3) the user can revisit the taught action model to refine them.
In this paper, we propose a novel SAT-based planning approach to solve totally ordered hierarchical planning problems. Our approach called “Tree-like Reduction Exploration” (Tree-REX) makes two contributions: (1) it allows to rapidly solve hierarchical planning problems by making effective use of incremental SAT solving, and (2) it implements an anytime approach that gradually improves plan quality (makespan) as time resources are allotted. Incremental SAT solving is important as it reduces the encoding volume of planning problems, it builds on information obtained from previous search iterations and speeds up the search for plans. We show that Tree-REX outperforms state-of-the-art SAT-based HTN planning with respect to run times and plan quality on most of the considered IPC domains.
The increasing presence of robots in industries has not gone unnoticed. Large industrial players have incorporated them into their production lines, but smaller companies hesitate due to high initial costs and the lack of programming expertise. In this work we introduce a framework that combines two disciplines, Programming by Demonstration and Automated Planning, to allow users without any programming knowledge to program a robot. The user teaches the robot atomic actions together with their semantic meaning and represents them in terms of preconditions and effects. Using these atomic actions the robot can generate action sequences autonomously to reach any goal given by the user. We evaluated the usability of our framework in terms of user experiments with a Baxter Research Robot and showed that it is well-adapted to users without any programming experience.
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