Body models in humans, animals, and robots: mechanisms and plasticity

Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These capabilities are also highly desirable in robots. They are displayed by machines to some extent - yet, as is so often the case, the artificial creatures are lagging behind. The key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. In the biological realm, evidence has been accumulated by diverse disciplines giving rise to the concepts of body image, body schema, and others. In robotics, a model of the robot is an indispensable component that enables to control the machine. In this article I compare the character of body representations in biology with their robotic counterparts and relate that to the differences in performance that we observe. I put forth a number of axes regarding the nature of such body models: fixed vs. plastic, amodal vs. modal, explicit vs. implicit, serial vs. parallel, modular vs. holistic, and centralized vs. distributed. An interesting trend emerges: on many of the axes, there is a sequence from robot body models, over body image, body schema, to the body representation in lower animals like the octopus. In some sense, robots have a lot in common with Ian Waterman - "the man who lost his body" - in that they rely on an explicit, veridical body model (body image taken to the extreme) and lack any implicit, multimodal representation (like the body schema) of their bodies. I will then detail how robots can inform the biological sciences dealing with body representations and finally, I will study which of the features of the "body in the brain" should be transferred to robots, giving rise to more adaptive and resilient, self-calibrating machines.Comment: 27 pages, 8 figure

Towards learning inverse kinematics with a neural network based tracking controller

Learning an inverse kinematic model of a robot is a well studied subject. However, achieving this without information about the geometric characteristics of the robot is less investigated. In this work, a novel control approach is presented based on a recurrent neural network. Without any prior knowledge about the robot, this control strategy learns to control the iCub’s robot arm online by solving the inverse kinematic problem in its control region. Because of its exploration strategy the robot starts to learn by generating and observing random motor behavior. The modulation and generalization capabilities of this approach are investigated as well

Advances in humanoid control and perception

One day there will be humanoid robots among us doing our boring, time-consuming, or dangerous tasks. They might cook a delicious meal for us or do the groceries. For this to become reality, many advances need to be made to the artificial intelligence of humanoid robots. The ever-increasing available computational processing power opens new doors for such advances. In this thesis we develop novel algorithms for humanoid control and vision that harness this power. We apply these methods on an iCub humanoid upper-body with 41 degrees of freedom. For control, we develop Natural Gradient Inverse Kinematics (NGIK), a sampling-based optimiser that applies natural evolution strategies to perform inverse kinematics. The resulting algorithm makes very few assumptions and gives much more freedom in definable constraints than its Jacobian-based counterparts. A special graph-building procedure is introduced to build Task-Relevant Roadmaps (TRM) by iteratively applying NGIK and storing the results. TRMs form searchable graphs of kinematic configurations on which a wide range of task-relevant humanoid movements can be planned. Through coordinating several instances of NGIK, a fast parallelised version of the TRM building algorithm is developed. To contrast the offline TRM algorithms, we also develop Natural Gradient Control which directly uses the optimisation pass in NGIK as an online control signal. For vision, we develop dynamic vision algorithms that form cyclic information flows that affect their own processing. Deep Attention Selective Networks (dasNet) implement feedback in convolutional neural networks through a gating mechanism that is steered by a policy. Through this feedback, dasNet can focus on different features in the image in light of previously gathered information and improve classification, with state-of-the- art results at the time of publication. Then, we develop PyraMiD-LSTM, which processes 3D volumetric data by employing a novel convolutional Long Short-Term Memory network (C-LSTM) to compute pyramidal contexts for every voxel, and combine them to perform segmentation. This resulted in state-of-the-art performance on a segmentation benchmark. The work on control and vision is integrated into an application on the iCub robot. A Fast-Weight PyraMiD-LSTM is developed that dynamically generates weights for a C-LSTM layer given actions of the robot. An explorative policy using NGC generates a stream of data, which the Fast-Weight PyraMiD-LSTM has to predict. The resulting integrated system learns to model the effects of head and hand movements and their effects on future visual input. To our knowledge, this is the first effective visual prediction system on an iCub

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Peripersonal Space in the Humanoid Robot iCub

Developing behaviours for interaction with objects close to the body is a primary goal for any organism to survive in the world. Being able to develop such behaviours will be an essential feature in autonomous humanoid robots in order to improve their integration into human environments. Adaptable spatial abilities will make robots safer and improve their social skills, human-robot and robot-robot collaboration abilities. This work investigated how a humanoid robot can explore and create action-based representations of its peripersonal space, the region immediately surrounding the body where reaching is possible without location displacement. It presents three empirical studies based on peripersonal space findings from psychology, neuroscience and robotics. The experiments used a visual perception system based on active-vision and biologically inspired neural networks. The first study investigated the contribution of binocular vision in a reaching task. Results indicated the signal from vergence is a useful embodied depth estimation cue in the peripersonal space in humanoid robots. The second study explored the influence of morphology and postural experience on confidence levels in reaching assessment. Results showed that a decrease of confidence when assessing targets located farther from the body, possibly in accordance to errors in depth estimation from vergence for longer distances. Additionally, it was found that a proprioceptive arm-length signal extends the robot’s peripersonal space. The last experiment modelled development of the reaching skill by implementing motor synergies that progressively unlock degrees of freedom in the arm. The model was advantageous when compared to one that included no developmental stages. The contribution to knowledge of this work is extending the research on biologically-inspired methods for building robots, presenting new ways to further investigate the robotic properties involved in the dynamical adaptation to body and sensing characteristics, vision-based action, morphology and confidence levels in reaching assessment.CONACyT, Mexico (National Council of Science and Technology

Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey

Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Projekt DEALPeer Reviewe

Sensorimotor representation learning for an "active self" in robots: A model survey

Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyse what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration

Development of Cognitive Capabilities in Humanoid Robots

Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate grand challenge for science and technology in general, and especially for the computational intelligence community. Recent theories in autonomous cognitive systems have focused on the close integration (grounding) of communication with perception, categorisation and action. Cognitive systems are essential for integrated multi-platform systems that are capable of sensing and communicating. This thesis presents a cognitive system for a humanoid robot that integrates abilities such as object detection and recognition, which are merged with natural language understanding and refined motor controls. The work includes three studies; (1) the use of generic manipulation of objects using the NMFT algorithm, by successfully testing the extension of the NMFT to control robot behaviour; (2) a study of the development of a robotic simulator; (3) robotic simulation experiments showing that a humanoid robot is able to acquire complex behavioural, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to cooperate with human users, and to adapt its abilities to changes in internal and environmental conditions. The model and the experimental results reported in this thesis, emphasise the importance of embodied cognition, i.e. the humanoid robot's physical interaction between its body and the environment

Learning-based methods for planning and control of humanoid robots

Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans. No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience. This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity. First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks. Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness. The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3