Motor memories of object dynamics are categorically organized

The ability to predict the dynamics of objects, linking applied force to motion, underlies our capacity to perform many of the tasks we carry out on a daily basis. Thus, a fundamental question is how the dynamics of the myriad objects we interact with are organized in memory. Using a custom-built three-dimensional robotic interface that allowed us to simulate objects of varying appearance and weight, we examined how participants learned the weights of sets of objects that they repeatedly lifted. We find strong support for the novel hypothesis that motor memories of object dynamics are organized categorically, in terms of families, based on covariation in their visual and mechanical properties. A striking prediction of this hypothesis, supported by our findings and not predicted by standard associative map models, is that outlier objects with weights that deviate from the family-predicted weight will never be learned despite causing repeated lifting errors.

Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment

Learning how to execute complex tasks involving multiple objects in a 3D world is challenging when there is no ground-truth information about the objects or any demonstration to learn from. When an agent only receives a signal from task-completion, this makes it challenging to learn the object-representations which support learning the correct object-interactions needed to complete the task. In this work, we formulate learning an attentive object dynamics model as a classification problem, using random object-images to define incorrect labels for our object-dynamics model. We show empirically that this enables object-representation learning that captures an object's category (is it a toaster?), its properties (is it on?), and object-relations (is something inside of it?). With this, our core learner (a relational RL agent) receives the dense training signal it needs to rapidly learn object-interaction tasks. We demonstrate results in the 3D AI2Thor simulated kitchen environment with a range of challenging food preparation tasks. We compare our method's performance to several related approaches and against the performance of an oracle: an agent that is supplied with ground-truth information about objects in the scene. We find that our agent achieves performance closest to the oracle in terms of both learning speed and maximum success rate.

Motor memories of object dynamics are categorically organized

The ability to predict the dynamics of objects, linking applied force to motion, underlies our capacity to perform many of the tasks we carry out on a daily basis. Thus, a fundamental question is how the dynamics of the myriad objects we interact with are organized in memory. Using a custom-built three-dimensional robotic interface that allowed us to simulate objects of varying appearance and weight, we examined how participants learned the weights of sets of objects that they repeatedly lifted. We find strong support for the novel hypothesis that motor memories of object dynamics are organized categorically, in terms of families, based on covariation in their visual and mechanical properties. A striking prediction of this hypothesis, supported by our findings and not predicted by standard associative map models, is that outlier objects with weights that deviate from the family-predicted weight will never be learned despite causing repeated lifting errors.

A PERSON IN A DIGITAL SOCIETY: SUBJECT VERSUS OBJECT

В статье рассматривается взаимодействие информационных структур цифрового общества и человека сквозь призму их характеристик как субъекта и объекта. На фоне противостояния двух основных тенденций развития современной мировой цивилизации - гуманизма и технократизма - анализируются возможности, механизмы, направления и противоречивые последствия субъектно-объектной динамики в отношениях «информационное пространство - информационный человек». Обосновывается положение о доминировании человека-субъекта как творца общества знания, вывод о необходимости разработки и сути соответствующего типа культуры в качестве главного фактора, обеспечивающего данный статус личности. The article deals with the interaction of information structures of digital society and man through the prism of their characteristics as a subject and object. Against the background of the opposition of two main trends in the development of modern world civilization - humanism and technocratism - the author analyzes the possibilities, mechanisms, directions and contradictory con-sequences of subject-object dynamics in the relationship «information space - information man». The article substantiates the position of the dominance of the human subject as the Creator of the knowledge society, the conclusion about the need to develop and essence of the corresponding type of culture as the main factor ensuring this status of the individual.

Physical Human–Robot Cooperation Based on Robust Motion Intention Estimation

SUMMARYCooperative transportation by human and robotic coworkers constitutes a challenging research field that could lead to promising technological achievements. Toward this direction, the present work demonstrates that, under a leader–follower architecture, where the human determines the object’s desired trajectory, complex cooperative object manipulation with minimal human effort may be achieved. More specifically, the robot estimates the object’s desired motion via a prescribed performance estimation law that drives the estimation error to an arbitrarily small residual set. Subsequently, the motion intention estimation is utilized in the object dynamics to determine the interaction force between the human and the object. Human effort reduction is then achieved via an impedance control scheme that employs the aforementioned estimations. The feedback relies exclusively on the robot’s force/torque, position as well as velocity measurements at its end effector, without incorporating any other information on the task. Moreover, an adaptive control scheme is adopted to relax the need for exact knowledge of the object dynamics. Finally, an extension for multiple robotic coworkers is studied and verified via simulation, while extensive experimental results for the single robot case clarify the proposed method and corroborate its efficiency.

Incorrect Simulation Results in OpenModelica

An adequacy of mathematical modelling of the technical object dynamics is always a challenge, and a user can make up his mind about the modelling package using simple test schemes. The article presents the schemes that are quite common from the point of view of an inexperienced user, but have specifics in mathematical modelling. The features of the schemes are that therein failures of equilibrium equations (such as the first Kirchhoff law) and continuity (such as the second Kirchhoff law) can occur. These features can lead to incorrect results when using the OpenModelica package, while the domestic PA9 and PRADIS packages used in modelling these schemes give the correct result. The article presents two simple schemes, the simulation result of which can be a priori estimated, and provides simulation results for three packages - OpenModelica, PA9, and PRADIS.

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

Motor memories in manipulation tasks are linked to contact goals between objects

Skilled manipulation requires forming memories of object dynamics, previously assumed to be associated with entire objects. However, we recently demonstrated that people can form multiple motor memories when explicitly instructed to move different locations on an object to different targets. Here, we show that separate motor memories can be learned for different contact goals, which involve a unique combination of a control point and target.