Learning to Interactively Learn and Assist

When deploying autonomous agents in the real world, we need effective ways of communicating objectives to them. Traditional skill learning has revolved around reinforcement and imitation learning, each with rigid constraints on the format of information exchanged between the human and the agent. While scalar rewards carry little information, demonstrations require significant effort to provide and may carry more information than is necessary. Furthermore, rewards and demonstrations are often defined and collected before training begins, when the human is most uncertain about what information would help the agent. In contrast, when humans communicate objectives with each other, they make use of a large vocabulary of informative behaviors, including non-verbal communication, and often communicate throughout learning, responding to observed behavior. In this way, humans communicate intent with minimal effort. In this paper, we propose such interactive learning as an alternative to reward or demonstration-driven learning. To accomplish this, we introduce a multi-agent training framework that enables an agent to learn from another agent who knows the current task. Through a series of experiments, we demonstrate the emergence of a variety of interactive learning behaviors, including information-sharing, information-seeking, and question-answering. Most importantly, we find that our approach produces an agent that is capable of learning interactively from a human user, without a set of explicit demonstrations or a reward function, and achieving significantly better performance cooperatively with a human than a human performing the task alone.Comment: AAAI 2020. Video overview at https://youtu.be/8yBvDBuAPrw, paper website with videos and interactive game at http://interactive-learning.github.io

Dual brain stimulation enhances interpersonal learning through spontaneous movement synchrony

Abstract Social interactive learning denotes the ability to acquire new information from a conspecific—a prerequisite for cultural evolution and survival. As inspired by recent neurophysiological research, here we tested whether social interactive learning can be augmented by exogenously synchronizing oscillatory brain activity across an instructor and a learner engaged in a naturalistic song-learning task. We used a dual brain stimulation protocol entailing the trans-cranial delivery of synchronized electric currents in two individuals simultaneously. When we stimulated inferior frontal brain regions, with 6 Hz alternating currents being in-phase between the instructor and the learner, the dyad exhibited spontaneous and synchronized body movement. Remarkably, this stimulation also led to enhanced learning performance. These effects were both phase- and frequency-specific: 6 Hz anti-phase stimulation or 10 Hz in-phase stimulation, did not yield comparable results. Furthermore, a mediation analysis disclosed that interpersonal movement synchrony acted as a partial mediator of the effect of dual brain stimulation on learning performance, i.e. possibly facilitating the effect of dual brain stimulation on learning. Our results provide a causal demonstration that inter-brain synchronization is a sufficient condition to improve real-time information transfer between pairs of individuals

IMPLEMENTASI NON TRADITIONAL WRITING TASK YANG DISISIPKAN PADA MODEL INTERACTIVE LECTURE DEMONSTRATION UNTUK MENINGKATKAN KEMAMPUAN KOGNITIF, SCIENTIFIC EXPLANATION DAN KETERAMPILAN MENULIS SISWA MTS PADA TOPIK KALOR

Penelitian ini bertujuan untuk mendapatkan gambaran tentang peningkatan kemampuan kognitif, scientific explanation dan kualitas tulisan siswa sebagai efek implementasi non traditional writing task yang disisipkan pada model interactive lecture demonstration. Hal ini dilatarbelakangi dari proses pembelajaran fisika di sekolah menengah yang konvensional dengan capaian kemampuan kognitif yang kurang, kurang terlibatnya siswa dalam pembelajaran sehingga tidak dapat memunculkan kemampuan scientific explanation serta kurangnya keinginan siswa untuk menulis suatu materi yang telah diajarkan guru dengan menggunakan kata- katanya sendiri. Metode yang digunakan dalam penelitian ini adalah true experimental design dengan desain penelitian pretest-posttest control group design. Ppenelitian ini dilakukan di salah satu MTs Negeri di kota Jakarta. Instrumen yang digunakan dalam pengambilan data adalah instrumen kemampuan kognitif berbentuk tes tertulis jenis pilihan ganda dan instrumen scientific explanation berbentuk tes tertulis jenis essay pada topik kalor. Hasil penelitian menunjukkan bahwa implementasi non traditional writing task yang disisipkan pada model interactive lecture demonstration pada kemampuan kognitif dan scientific explanation pada kategori sedang dengan masing-masing nilai gain yang dinormalisasi adalah 〈g〉 = 0,46 dan 〈g〉 = 0,44. Hasil penelitian juga menunjukkan peningkatan kualitas karya tulis siswa berada pada kategori cukup, dengan nilai gain yang dinormalisasi untuk peningkatan kualitas tulisan 1 dan 4 〈g〉 = 0,32 , kualitas tulisan 2 dan 4 〈g〉 = 0,45 0,45, kualitas tulisan 3 dan 4 〈g〉 = 0,34 dan kualitas tulisan 2 dan 3 〈g〉 = 0,3 .----------This study aims to obtain an overview of the improvement of cognitive abilities, scientific explanation and the quality of students' writing as a result of the implementation of non traditional writing tasks inserted in the interactive lecture demonstration model. This is the background of physics learning process in conventional high school with the achievement of less cognitive ability, less involvement of students in learning so that can not bring up the ability of scientific explanation and lack of students desire to write a material that has been taught the teacher by using his own words. The method used in this research is true experimental design with pretest-posttest control group design design. This research was conducted in one of MTs Negeri in Jakarta. Instruments used in data retrieval are cognitive ability instruments in the form of written tests of multiple choice and scientific explanation instruments in the form of written tests of essays on the topic of heat. The results showed that the non-traditional writing task implementation which was inserted in the interactive lecture demonstration model on the cognitive and scientific explanation ability in the medium category with each normalized gain value was = 0.46 and = 0.44. The results of the study also showed that the improvement of the students' writing quality was in sufficient category, with normalized gain values for writing quality improvement 1 and 4 = 0.32, writing quality 2 and 4 = 0.45 0.45, writing quality 3 and 4 = 0.34 and writing quality 2 and 3 = 0.3

Supporting GUI exploration through USS tool

Advances in usability and design techniques (e.g. user-centered design) try to facilitate the use of interactive systems. However, users still have to adapt to interactive systems, i.e. they have to learn the steps required to accomplish a task either by trial and error or by obtaining help. While advanced users are usually able to adapt without much effort this is far from being the case with beginners. Some interactive systems offer different interaction styles in an attempt to meet the needs of all types of user but this is not the case with all interactive systems. In this sense, we present an approach to support the use of any interactive system making use of enriched models and picture-driven computing to achieve tasks automation. The USS tool (User Support System) is the basis to the adaptation of interactive systems accordingly to the users' needs. The approach provides the foundation for the addition of help (based on demonstration) to any graphical user interfaces (GUI) facilitating learning and use. The work is illustrated by a case study and completed with a preliminary user evaluation which provides insights about the validity of the approach.info:eu-repo/semantics/publishedVersio

Robot Learning Dual-Arm Manipulation Tasks by Trial-and-Error and Multiple Human Demonstrations

In robotics, there is a need of an interactive and expedite learning method as experience is expensive. In this research, we propose two different methods to make a humanoid robot learn manipulation tasks: Learning by trial-and-error, and Learning from demonstrations. Just like the way a child learns a new task assigned to him by trying all possible alternatives and further learning from his mistakes, the robot learns in the same manner in learning by trial-and error. We used Q-learning algorithm, in which the robot tries all the possible ways to do a task and creates a matrix that consists of Q-values based on the rewards it received for the actions performed. Using this method, the robot was made to learn dance moves based on a music track. Robot Learning from Demonstrations (RLfD) enable a human user to add new capabilities to a robot in an intuitive manner without explicitly reprogramming it. In this method, the robot learns skill from demonstrations performed by a human teacher. The robot extracts features from each demonstration called as key-points and learns a model of the demonstrated task or trajectory using Hidden Markov Model (HMM). The learned model is further used to produce a generalized trajectory. In the end, we discuss the differences between two developed systems and make conclusions based on the experiments performed

Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation

Reinforcement learning (RL) has achieved tremendous success as a general framework for learning how to make decisions. However, this success relies on the interactive hand-tuning of a reward function by RL experts. On the other hand, inverse reinforcement learning (IRL) seeks to learn a reward function from readily-obtained human demonstrations. Yet, IRL suffers from two major limitations: 1) reward ambiguity - there are an infinite number of possible reward functions that could explain an expert's demonstration and 2) heterogeneity - human experts adopt varying strategies and preferences, which makes learning from multiple demonstrators difficult due to the common assumption that demonstrators seeks to maximize the same reward. In this work, we propose a method to jointly infer a task goal and humans' strategic preferences via network distillation. This approach enables us to distill a robust task reward (addressing reward ambiguity) and to model each strategy's objective (handling heterogeneity). We demonstrate our algorithm can better recover task reward and strategy rewards and imitate the strategies in two simulated tasks and a real-world table tennis task.Comment: In Proceedings of the 2020 ACM/IEEE In-ternational Conference on Human-Robot Interaction (HRI '20), March 23 to 26, 2020, Cambridge, United Kingdom.ACM, New York, NY, USA, 10 page