We introduce a novel paradigm for robot pro- gramming with which we aim to make robot programming more accessible for unexperienced users. In order to do so we incorporate two major components in one single framework: autonomous skill acquisition by robotic playing and visual programming. Simple robot program skeletons solving a task for one specific situation, so-called basic behaviours, are provided by the user. The robot then learns how to solve the same task in many different situations by autonomous playing which reduces the barrier for unexperienced robot programmers. Programmers can use a mix of visual programming and kinesthetic teaching in order to provide these simple program skeletons. The robot program can be implemented interactively by programming parts with visual programming and kinesthetic teaching. We further integrate work on experience-based skill-centric robot software testing which enables the user to continuously test implemented skills without having to deal with the details of specific components.
@cite_17 follow a different approach with their system called (ROSCo). Generic parametrised skills are developed by experts in the respective field. End-users can combine them by visual programming in the form of hierarchical finite state machines (HFSM). These state machines are a generic representation of a skill which then can be deployed. Other users can load these state machines and adapt the skill to their local environment, e.g. by teaching where certain behaviours should be applied to. Our system uses a mixed paradigm in which the generic skills are either created by the end-user by visual programming and subsequent autonomous playing or by field experts which make the corresponding controller available.
@cite_17
2014073163 1993309788 1990671169 2122514299
We introduce ROS Commander (ROSCo), an open source system that enables expert users to construct, share, and deploy robot behaviors for home robots. A user builds a behavior in the form of a Hierarchical Finite State Machine (HFSM) out of generic, parameterized building blocks, with a real robot in the develop and test loop. Once constructed, users save behaviors in an open format for direct use with robots, or for use as parts of new behaviors. When the system is deployed, a user can show the robot where to apply behaviors relative to fiducial markers (AR Tags), which allows the robot to quickly become operational in a new environment. We show evidence that the underlying state machine representation and current building blocks are capable of spanning a variety of desirable behaviors for home robots, such as opening a refrigerator door with two arms, flipping a light switch, unlocking a door, and handing an object to someone. Our experiments show that sensor-driven behaviors constructed with ROSCo can be executed in realistic home environments with success rates between 80 and 100 . We conclude by describing a test in the home of a person with quadriplegia, in which the person was able to automate parts of his home using previously-built behaviors. Inspired by how humans learn dynamic motor skills through a progressive process of coaching and practices, we introduce an intuitive and interactive framework for developing dynamic controllers. The user only needs to provide a primitive initial controller and high-level, human-readable instructions as if s he is coaching a human trainee, while the character has the ability to interpret the abstract instructions, accumulate the knowledge from the coach, and improve its skill iteratively. We introduce “control rigs” as an intermediate layer of control module to facilitate the mapping between high-level instructions and low-level control variables. Control rigs also utilize the human coach's knowledge to reduce the search space for control optimization. In addition, we develop a new sampling-based optimization method, Covariance Matrix Adaptation with Classification (CMA-C), to efficiently compute-control rig parameters. Based on the observation of human ability to “learn from failure”, CMA-C utilizes the failed simulation trials to approximate an infeasible region in the space of control rig parameters, resulting a faster convergence for the CMA optimization. We demonstrate the design process of complex dynamic controllers using our framework, including precision jumps, turnaround jumps, monkey vaults, drop-and-rolls, and wall-backflips. HighlightsWe integrate user appraisals in a POMDP-based dialogue manager procedure.We employ additional socially-inspired rewards in a RL setup to guide the learning.A unified framework for speeding up the policy optimisation and user adaptation.We consider a potential-based reward shaping with a sample efficient RL algorithm.Evaluated using both user simulator (information retrieval) and user trials (HRI). This paper investigates some conditions under which polarized user appraisals gathered throughout the course of a vocal interaction between a machine and a human can be integrated in a reinforcement learning-based dialogue manager. More specifically, we discuss how this information can be cast into socially-inspired rewards for speeding up the policy optimisation for both efficient task completion and user adaptation in an online learning setting. For this purpose a potential-based reward shaping method is combined with a sample efficient reinforcement learning algorithm to offer a principled framework to cope with these potentially noisy interim rewards. The proposed scheme will greatly facilitate the system's development by allowing the designer to teach his system through explicit positive negative feedbacks given as hints about task progress, in the early stage of training. At a later stage, the approach will be used as a way to ease the adaptation of the dialogue policy to specific user profiles. Experiments carried out using a state-of-the-art goal-oriented dialogue management framework, the Hidden Information State (HIS), support our claims in two configurations: firstly, with a user simulator in the tourist information domain (and thus simulated appraisals), and secondly, in the context of man-robot dialogue with real user trials. Most previous work on trainable language generation has focused on two paradigms: (a) using a generation decisions of an existing generator. Both approaches rely on the existence of a handcrafted generation component, which is likely to limit their scalability to new domains. The first contribution of this article is to present Bagel, a fully data-driven generation method that treats the language generation task as a search for the most likely sequence of semantic concepts and realization phrases, according to Factored Language Models (FLMs). As domain utterances are not readily available for most natural language generation tasks, a large creative effort is required to produce the data necessary to represent human linguistic variation for nontrivial domains. This article is based on the assumption that learning to produce paraphrases can be facilitated by collecting data from a large sample of untrained annotators using crowdsourcing—rather than a few domain experts—by relying on a coarse meaning representation. A second contribution of this article is to use crowdsourced data to show how dialogue naturalness can be improved by learning to vary the output utterances generated for a given semantic input. Two data-driven methods for generating paraphrases in dialogue are presented: (a) by sampling from the n-best list of realizations produced by Bagel's FLM reranker; and (b) by learning a structured perceptron predicting whether candidate realizations are valid paraphrases. We train Bagel on a set of 1,956 utterances produced by 137 annotators, which covers 10 types of dialogue acts and 128 semantic concepts in a tourist information system for Cambridge. An automated evaluation shows that Bagel outperforms utterance class LM baselines on this domain. A human evaluation of 600 resynthesized dialogue extracts shows that Bagel's FLM output produces utterances comparable to a handcrafted baseline, whereas the perceptron classifier performs worse. Interestingly, human judges find the system sampling from the n-best list to be more natural than a system always returning the first-best utterance. The judges are also more willing to interact with the n-best system in the future. These results suggest that capturing the large variation found in human language using data-driven methods is beneficial for dialogue interaction.