arXiv subjects

Jun Takamatsu

Publications and source records attributed to Jun Takamatsu.

35 records · Page 2Linked to original sources

arXiv2023

ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application

This paper demonstrates how OpenAI's ChatGPT can be used in a few-shot setting to convert natural language instructions into a sequence of executable robot actions. The paper proposes easy-to-customize input prompts for ChatGPT that meet common requirements in practical applications, such as easy integration with robot execution systems and applicability to various environments while minimizing the impact of ChatGPT's token limit. The prompts encourage ChatGPT to output a sequence of predefined robot actions, represent the operating environment in a formalized style, and infer the updated state of the operating environment. Experiments confirmed that the proposed prompts enable ChatGPT to act according to requirements in various environments, and users can adjust ChatGPT's output with natural language feedback for safe and robust operation. The proposed prompts and source code are open-source and publicly available at https://github.com/microsoft/ChatGPT-Robot-Manipulation-Prompts

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arXiv2023

GPT Models Meet Robotic Applications: Co-Speech Gesturing Chat System

This technical paper introduces a chatting robot system that utilizes recent advancements in large-scale language models (LLMs) such as GPT-3 and ChatGPT. The system is integrated with a co-speech gesture generation system, which selects appropriate gestures based on the conceptual meaning of speech. Our motivation is to explore ways of utilizing the recent progress in LLMs for practical robotic applications, which benefits the development of both chatbots and LLMs. Specifically, it enables the development of highly responsive chatbot systems by leveraging LLMs and adds visual effects to the user interface of LLMs as an additional value. The source code for the system is available on GitHub for our in-house robot (https://github.com/microsoft/LabanotationSuite/tree/master/MSRAbotChatSimulation) and GitHub for Toyota HSR (https://github.com/microsoft/GPT-Enabled-HSR-CoSpeechGestures).

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arXiv2023

Interactive Task Encoding System for Learning-from-Observation

We present the Interactive Task Encoding System (ITES) for teaching robots to perform manipulative tasks. ITES is designed as an input system for the Learning-from-Observation (LfO) framework, which enables household robots to be programmed using few-shot human demonstrations without the need for coding. In contrast to previous LfO systems that rely solely on visual demonstrations, ITES leverages both verbal instructions and interaction to enhance recognition robustness, thus enabling multimodal LfO. ITES identifies tasks from verbal instructions and extracts parameters from visual demonstrations. Meanwhile, the recognition result was reviewed by the user for interactive correction. Evaluations conducted on a real robot demonstrate the successful teaching of multiple operations for several scenarios, suggesting the usefulness of ITES for multimodal LfO. The source code is available at https://github.com/microsoft/symbolic-robot-teaching-interface.

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arXiv2023

Applying Learning-from-observation to household service robots: three common-sense formulation

Utilizing a robot in a new application requires the robot to be programmed at each time. To reduce such programmings efforts, we have been developing ``Learning-from-observation (LfO)'' that automatically generates robot programs by observing human demonstrations. One of the main issues with introducing this LfO system into the domain of household tasks is the cluttered environments, which cause difficulty in determining which elements are important for task execution when observing demonstrations. To overcome this issue, it is necessary for the system to have common sense shared with the human demonstrator. This paper addresses three relationships that LfO in the household domain should focus on when observing demonstrations and proposes representations to describe the common sense used by the demonstrator for optimal execution of task sequences. Specifically, the paper proposes to use labanotation to describe the postures between the environment and the robot, contact-webs to describe the grasping methods between the robot and the tool, and physical and semantic constraints to describe the motions between the tool and the environment. Then, based on these representations, the paper formulates task models, machine-independent robot programs, that indicate what to do and how to do. Third, the paper explains the task encoder to obtain task models and task decoder to execute the task models on the robot hardware. Finally, this paper presents how the system actually works through several example scenes.

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arXiv2023

Task-sequencing Simulator: Integrated Machine Learning to Execution Simulation for Robot Manipulation

A task-sequencing simulator in robotics manipulation to integrate simulation-for-learning and simulation-for-execution is introduced. Unlike existing machine-learning simulation where a non-decomposed simulation is used to simulate a training scenario, the task-sequencing simulator runs a composed simulation using building blocks. This way, the simulation-for-learning is structured similarly to a multi-step simulation-for-execution. To compose both learning and execution scenarios, a unified trainable-and-composable description of blocks called a concept model is proposed and used. Using the simulator design and concept models, a reusable simulator for learning different tasks, a common-ground system for learning-to-execution, simulation-to-real is achieved and shown.

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arXiv2022

Learning-from-Observation System Considering Hardware-Level Reusability

Robot developers develop various types of robots for satisfying users' various demands. Users' demands are related to their backgrounds and robots suitable for users may vary. If a certain developer would offer a robot that is different from the usual to a user, the robot-specific software has to be changed. On the other hand, robot-software developers would like to reuse their developed software as much as possible to reduce their efforts. We propose the system design considering hardware-level reusability. For this purpose, we begin with the learning-from-observation framework. This framework represents a target task in robot-agnostic representation, and thus the represented task description can be shared with various robots. When executing the task, it is necessary to convert the robot-agnostic description into commands of a target robot. To increase the reusability, first, we implement the skill library, robot motion primitives, only considering a robot hand and we regarded that a robot was just a carrier to move the hand on the target trajectory. The skill library is reusable if we would like to the same robot hand. Second, we employ the generic IK solver to quickly swap a robot. We verify the hardware-level reusability by applying two task descriptions to two different robots, Nextage and Fetch.

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arXiv2022

Design strategies for controlling neuron-connected robots using reinforcement learning

Despite the growing interest in robot control utilizing the computation of biological neurons, context-dependent behavior by neuron-connected robots remains a challenge. Context-dependent behavior here is defined as behavior that is not the result of a simple sensory-motor coupling, but rather based on an understanding of the task goal. This paper proposes design principles for training neuron-connected robots based on task goals to achieve context-dependent behavior. First, we employ deep reinforcement learning (RL) to enable training that accounts for goal achievements. Second, we propose a neuron simulator as a probability distribution based on recorded neural data, aiming to represent physiologically valid neural dynamics while avoiding complex modeling with high computational costs. Furthermore, we propose to update the simulators during the training to bridge the gap between the simulation and the real settings. The experiments showed that the robot gradually learned context-dependent behaviors in pole balancing and robot navigation tasks. Moreover, the learned policies were valid for neural simulators based on novel neural data, and the task performance increased by updating the simulators during training. These results suggest the effectiveness of the proposed design principle for the context-dependent behavior of neuron-connected robots.

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arXiv2022

Task-grasping from human demonstration

A challenge in robot grasping is to achieve task-grasping which is to select a grasp that is advantageous to the success of tasks before and after grasps. One of the frameworks to address this difficulty is Learning-from-Observation (LfO), which obtains various hints from human demonstrations. This paper solves three issues in the grasping skills in the LfO framework: 1) how to functionally mimic human-demonstrated grasps to robots with limited grasp capability, 2) how to coordinate grasp skills with reaching body mimicking, 3) how to robustly perform grasps under object pose and shape uncertainty. A deep reinforcement learning using contact-web based rewards and domain randomization of approach directions is proposed to achieve such robust mimicked grasping skills. Experiment results show that the trained grasping skills can be applied in an LfO system and executed on a real robot. In addition, it is shown that the trained skill is robust to errors in the object pose and to the uncertainty of the object shape and can be combined with various reach-coordination.

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arXiv2021

Soft-Jig: A Flexible Sensing Jig for Simultaneously Fixing and Estimating Orientation of Assembly Parts

For assembly tasks, it is essential to firmly fix target parts and to accurately estimate their poses. Several rigid jigs for individual parts are frequently used in assembly factories to achieve precise and time-efficient product assembly. However, providing customized jigs is time-consuming. In this study, to address the lack of versatility in the shapes the jigs can be used for, we developed a flexible jig with a soft membrane including transparent beads and oil with a tuned refractive index. The bead-based jamming transition was accomplished by discharging only oil enabling a part to be firmly fixed. Because the two cameras under the jig are able to capture membrane shape changes, we proposed a sensing method to estimate the orientation of the part based on the behaviors of markers created on the jig's inner surface. Through estimation experiments, the proposed system could estimate the orientation of a cylindrical object with a diameter larger than 50 mm and an RMSE of less than 3 degrees.

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arXiv2021

Robotic Waste Sorter with Agile Manipulation and Quickly Trainable Detector

Owing to human labor shortages, the automation of labor-intensive manual waste-sorting is needed. The goal of automating waste-sorting is to replace the human role of robust detection and agile manipulation of waste items with robots. To achieve this, we propose three methods. First, we provide a combined manipulation method using graspless push-and-drop and pick-and-release manipulation. Second, we provide a robotic system that can automatically collect object images to quickly train a deep neural-network model. Third, we provide a method to mitigate the differences in the appearance of target objects from two scenes: one for dataset collection and the other for waste sorting in a recycling factory. If differences exist, the performance of a trained waste detector may decrease. We address differences in illumination and background by applying object scaling, histogram matching with histogram equalization, and background synthesis to the source target-object images. Via experiments in an indoor experimental workplace for waste-sorting, we confirm that the proposed methods enable quick collection of the training image sets for three classes of waste items (i.e., aluminum can, glass bottle, and plastic bottle) and detection with higher performance than the methods that do not consider the differences. We also confirm that the proposed method enables the robot quickly manipulate the objects.

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arXiv2021

Assembly Sequences Based on Multiple Criteria Against Products with Deformable Parts

Aiming to generate easy-to-handle assembly sequences for robotic assembly, this study tackles assembly sequence generation by considering two tradeoff objectives: (1) insertion conditions and (2) degrees of constraints among assembled parts. We propose a multiobjective genetic algorithm to balance these two objectives for generating assembly sequences. Furthermore, the method of extracting part relation matrices including interference-free, insertion, and degree of constraint matrices is extended for application to 3D computer-aided design (CAD) models, including deformable parts. The interference of deformable parts with other parts can be easily investigated by scaling parts. A simulation experiment was conducted using the proposed method, and the results show the possibility of obtaining Pareto-optimal solutions of assembly sequences for a 3D CAD model with 33 parts including a deformable part. This approach can potentially be extended to handle various types of deformable parts and to explore graspable sequences during assembly operations.

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arXiv2021

Soft-Jig-Driven Assembly Operations

To design a general-purpose assembly robot system that can handle objects of various shapes, we propose a soft jig that fits to the shapes of assembly parts. The functionality of the soft jig is based on a jamming gripper developed in the field of soft robotics. The soft jig has a bag covered with a malleable silicone membrane, which has high friction, elongation, and contraction rates for keeping parts fixed. The bag is filled with glass beads to achieve a jamming transition. We propose a method to configure parts-fixing on the soft jig based on contact relations, reachable directions, and the center of gravity of the parts that are fixed on the jig. The usability of the soft jig was evaluated in terms of the fixing performance and versatility for various shapes and postures of parts.

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arXiv2021

Semantic constraints to represent common sense required in household actions for multi-modal Learning-from-observation robot

The paradigm of learning-from-observation (LfO) enables a robot to learn how to perform actions by observing human-demonstrated actions. Previous research in LfO have mainly focused on the industrial domain which only consist of the observable physical constraints between a manipulating tool and the robot's working environment. In order to extend this paradigm to the household domain which consists non-observable constraints derived from a human's common sense; we introduce the idea of semantic constraints. The semantic constraints are represented similar to the physical constraints by defining a contact with an imaginary semantic environment. We thoroughly investigate the necessary and sufficient set of contact state and state transitions to understand the different types of physical and semantic constraints. We then apply our constraint representation to analyze various actions in top hit household YouTube videos and real home cooking recordings. We further categorize the frequently appearing constraint patterns into physical, semantic, and multistage task groups and verify that these groups are not only necessary but a sufficient set for covering standard household actions. Finally, we conduct a preliminary experiment using textual input to explore the possibilities of combining verbal and visual input for recognizing the task groups. Our results provide promising directions for incorporating common sense in the literature of robot teaching.

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arXiv2020

Toward an Affective Touch Robot: Subjective and Physiological Evaluation of Gentle Stroke Motion Using a Human-Imitation Hand

Affective touch offers positive psychological and physiological benefits such as the mitigation of stress and pain. If a robot could realize human-like affective touch, it would open up new application areas, including supporting care work. In this research, we focused on the gentle stroking motion of a robot to evoke the same emotions that human touch would evoke: in other words, an affective touch robot. We propose a robot that is able to gently stroke the back of a human using our designed human-imitation hand. To evaluate the emotional effects of this affective touch, we compared the results of a combination of two agents (the human-imitation hand and the human hand), at two stroke speeds (3 and 30 cm/s). The results of the subjective and physiological evaluations highlighted the following three findings: 1) the subjects evaluated strokes similarly with regard to the stroke speed of the human and human-imitation hand, in both the subjective and physiological evaluations; 2) the subjects felt greater pleasure and arousal at the faster stroke rate (30 cm/s rather than 3 cm/s); and 3) poorer fitting of the human-imitation hand due to the bending of the back had a negative emotional effect on the subjects.

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arXiv2020

A Learning-from-Observation Framework: One-Shot Robot Teaching for Grasp-Manipulation-Release Household Operations

A household robot is expected to perform various manipulative operations with an understanding of the purpose of the task. To this end, a desirable robotic application should provide an on-site robot teaching framework for non-experts. Here we propose a Learning-from-Observation (LfO) framework for grasp-manipulation-release class household operations (GMR-operations). The framework maps human demonstrations to predefined task models through one-shot teaching. Each task model contains both high-level knowledge regarding the geometric constraints and low-level knowledge related to human postures. The key idea is to design a task model that 1) covers various GMR-operations and 2) includes human postures to achieve tasks. We verify the applicability of our framework by testing an operational LfO system with a real robot. In addition, we quantify the coverage of the task model by analyzing online videos of household operations. In the context of one-shot robot teaching, the contribution of this study is a framework that 1) covers various GMR-operations and 2) mimics human postures during the operations.

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arXiv2020

Control of Walking Assist Exoskeleton with Time-delay Based on the Prediction of Plantar Force

Many kinds of lower-limb exoskeletons were developed for walking assistance. However, when controlling these exoskeletons, time-delay due to the computation time and the communication delays is still a general problem. In this research, we propose a novel method to prevent the time-delay when controlling a walking assist exoskeleton by predicting the future plantar force and walking status. By using Long Short-Term Memory and a fully-connected network, the plantar force can be predicted using only data measured by inertial measurement unit sensors, not only during the walking period but also at the start and end of walking. From the predicted plantar force, the walking status and the desired assistance timing can also be determined. By considering the time-delay and sending the control commands beforehand, the exoskeleton can be moved precisely on the desired assistance timing. In experiments, the prediction accuracy of the plantar force and the assistance timing are confirmed. The performance of the proposed method is also evaluated by using the trained model to control the exoskeleton.

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arXiv2018

Multi-View Inpainting for RGB-D Sequence

In this work we propose a novel approach to remove undesired objects from RGB-D sequences captured with freely moving cameras, which enables static 3D reconstruction. Our method jointly uses existing information from multiple frames as well as generates new one via inpainting techniques. We use balanced rules to select source frames; local homography based image warping method for alignment and Markov random field (MRF) based approach for combining existing information. For the left holes, we employ exemplar based multi-view inpainting method to deal with the color image and coherently use it as guidance to complete the depth correspondence. Experiments show that our approach is qualified for removing the undesired objects and inpainting the holes.

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