124 research outputs found
Daily Assistive Modular Robot Design Based on Multi-Objective Black-Box Optimization
The range of robot activities is expanding from industries with fixed
environments to diverse and changing environments, such as nursing care support
and daily life support. In particular, autonomous construction of robots that
are personalized for each user and task is required. Therefore, we develop an
actuator module that can be reconfigured to various link configurations, can
carry heavy objects using a locking mechanism, and can be easily operated by
human teaching using a releasing mechanism. Given multiple target coordinates,
a modular robot configuration that satisfies these coordinates and minimizes
the required torque is automatically generated by Tree-structured Parzen
Estimator (TPE), a type of black-box optimization. Based on the obtained
results, we show that the robot can be reconfigured to perform various
functions such as moving monitors and lights, serving food, and so on.Comment: Accepted at IROS2023, website -
https://haraduka.github.io/auto-modular-design
Singularity-free Aerial Deformation by Two-dimensional Multilinked Aerial Robot with 1-DoF Vectorable Propeller
Two-dimensional multilinked structures can benefit aerial robots in both
maneuvering and manipulation because of their deformation ability. However,
certain types of singular forms must be avoided during deformation. Hence, an
additional 1 Degrees-of-Freedom (DoF) vectorable propeller is employed in this
work to overcome singular forms by properly changing the thrust direction. In
this paper, we first extend modeling and control methods from our previous
works for an under-actuated model whose thrust forces are not unidirectional.
We then propose a planning method for the vectoring angles to solve the
singularity by maximizing the controllability under arbitrary robot forms.
Finally, we demonstrate the feasibility of the proposed methods by experiments
where a quad-type model is used to perform trajectory tracking under
challenging forms, such as a line-shape form, and the deformation passing these
challenging forms
A method for Selecting Scenes and Emotion-based Descriptions for a Robot's Diary
In this study, we examined scene selection methods and emotion-based
descriptions for a robot's daily diary. We proposed a scene selection method
and an emotion description method that take into account semantic and affective
information, and created several types of diaries. Experiments were conducted
to examine the change in sentiment values and preference of each diary, and it
was found that the robot's feelings and impressions changed more from date to
date when scenes were selected using the affective captions. Furthermore, we
found that the robot's emotion generally improves the preference of the robot's
diary regardless of the scene it describes. However, presenting negative or
mixed emotions at once may decrease the preference of the diary or reduce the
robot's robot-likeness, and thus the method of presenting emotions still needs
further investigation.Comment: 6 pages, 5 figures, ROMAN 202
Recognition of Heat-Induced Food State Changes by Time-Series Use of Vision-Language Model for Cooking Robot
Cooking tasks are characterized by large changes in the state of the food,
which is one of the major challenges in robot execution of cooking tasks. In
particular, cooking using a stove to apply heat to the foodstuff causes many
special state changes that are not seen in other tasks, making it difficult to
design a recognizer. In this study, we propose a unified method for recognizing
changes in the cooking state of robots by using the vision-language model that
can discriminate open-vocabulary objects in a time-series manner. We collected
data on four typical state changes in cooking using a real robot and confirmed
the effectiveness of the proposed method. We also compared the conditions and
discussed the types of natural language prompts and the image regions that are
suitable for recognizing the state changes.Comment: Accepted at IAS18-202
Robotic Applications of Pre-Trained Vision-Language Models to Various Recognition Behaviors
In recent years, a number of models that learn the relations between vision
and language from large datasets have been released. These models perform a
variety of tasks, such as answering questions about images, retrieving
sentences that best correspond to images, and finding regions in images that
correspond to phrases. Although there are some examples, the connection between
these pre-trained vision-language models and robotics is still weak. If they
are directly connected to robot motions, they lose their versatility due to the
embodiment of the robot and the difficulty of data collection, and become
inapplicable to a wide range of bodies and situations. Therefore, in this
study, we categorize and summarize the methods to utilize the pre-trained
vision-language models flexibly and easily in a way that the robot can
understand, without directly connecting them to robot motions. We discuss how
to use these models for robot motion selection and motion planning without
re-training the models. We consider five types of methods to extract
information understandable for robots, and show the results of state
recognition, object recognition, affordance recognition, relation recognition,
and anomaly detection based on the combination of these five methods. We expect
that this study will add flexibility and ease-of-use, as well as new
applications, to the recognition behavior of existing robots
Binary State Recognition by Robots using Visual Question Answering of Pre-Trained Vision-Language Model
Recognition of the current state is indispensable for the operation of a
robot. There are various states to be recognized, such as whether an elevator
door is open or closed, whether an object has been grasped correctly, and
whether the TV is turned on or off. Until now, these states have been
recognized by programmatically describing the state of a point cloud or raw
image, by annotating and learning images, by using special sensors, etc. In
contrast to these methods, we apply Visual Question Answering (VQA) from a
Pre-Trained Vision-Language Model (PTVLM) trained on a large-scale dataset, to
such binary state recognition. This idea allows us to intuitively describe
state recognition in language without any re-training, thereby improving the
recognition ability of robots in a simple and general way. We summarize various
techniques in questioning methods and image processing, and clarify their
properties through experiments
Online Estimation of Self-Body Deflection With Various Sensor Data Based on Directional Statistics
In this paper, we propose a method for online estimation of the robot's
posture. Our method uses von Mises and Bingham distributions as probability
distributions of joint angles and 3D orientation, which are used in directional
statistics. We constructed a particle filter using these distributions and
configured a system to estimate the robot's posture from various sensor
information (e.g., joint encoders, IMU sensors, and cameras). Furthermore,
unlike tangent space approximations, these distributions can handle global
features and represent sensor characteristics as observation noises. As an
application, we show that the yaw drift of a 6-axis IMU sensor can be
represented probabilistically to prevent adverse effects on attitude
estimation. For the estimation, we used an approximate model that assumes the
actual robot posture can be reproduced by correcting the joint angles of a
rigid body model. In the experiment part, we tested the estimator's
effectiveness by examining that the joint angles generated with the approximate
model can be estimated using the link pose of the same model. We then applied
the estimator to the actual robot and confirmed that the gripper position could
be estimated, thereby verifying the validity of the approximate model in our
situation.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
- …