The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Acquisition and distribution of synergistic reactive control skills

Learning from demonstration is an afficient way to attain a new skill. In the context of autonomous robots, using a demonstration to teach a robot accelerates the robot learning process significantly. It helps to identify feasible solutions as starting points for future exploration or to avoid actions that lead to failure. But the acquisition of pertinent observationa is predicated on first segmenting the data into meaningful sequences. These segments form the basis for learning models capable of recognising future actions and reconstructing the motion to control a robot. Furthermore, learning algorithms for generative models are generally not tuned to produce stable trajectories and suffer from parameter redundancy for high degree of freedom robots This thesis addresses these issues by firstly investigating algorithms, based on dynamic programming and mixture models, for segmentation sensitivity and recognition accuracy on human motion capture data sets of repetitive and categorical motion classes. A stability analysis of the non-linear dynamical systems derived from the resultant mixture model representations aims to ensure that any trajectories converge to the intended target motion as observed in the demonstrations. Finally, these concepts are extended to humanoid robots by deploying a factor analyser for each mixture model component and coordinating the structure into a low dimensional representation of the demonstrated trajectories. This representation can be constructed as a correspondence map is learned between the demonstrator and robot for joint space actions. Applying these algorithms for demonstrating movement skills to robot is a further step towards autonomous incremental robot learning

Online Geometric Human Interaction Segmentation and Recognition

The goal of this work is the temporal localization and recognition of binary people interactions in video. Human-human interaction detection is one of the core problems in video analysis. It has many applications such as in video surveillance, video search and retrieval, human-computer interaction, and behavior analysis for safety and security. Despite the sizeable literature in the area of activity and action modeling and recognition, the vast majority of the approaches make the assumption that the beginning and the end of the video portion containing the action or the activity of interest is known. In other words, while a significant effort has been placed on the recognition, the spatial and temporal localization of activities, i.e. the detection problem, has received considerably less attention. Even more so, if the detection has to be made in an online fashion, as opposed to offline. The latter condition is imposed by almost the totality of the state-of-the-art, which makes it intrinsically unsuited for real-time processing. In this thesis, the problem of event localization and recognition is addressed in an online fashion. The main assumption is that an interaction, or an activity is modeled by a temporal sequence. One of the main challenges is the development of a modeling framework able to capture the complex variability of activities, described by high dimensional features. This is addressed by the combination of linear models with kernel methods. In particular, the parity space theory for detection, based on Euclidean geometry, is augmented to be able to work with kernels, through the use of geometric operators in Hilbert space. While this approach is general, here it is applied to the detection of human interactions. It is tested on a publicly available dataset and on a large and challenging, newly collected dataset. An extensive testing of the approach indicates that it sets a new state-of-the-art under several performance measures, and that it holds the promise to become an effective building block for the analysis in real-time of human behavior from video

Adaptive control of compliant robots with Reservoir Computing

In modern society, robots are increasingly used to handle dangerous, repetitive and/or heavy tasks with high precision. Because of the nature of the tasks, either being dangerous, high precision or simply repetitive, robots are usually constructed with high torque motors and sturdy materials, that makes them dangerous for humans to handle. In a car-manufacturing company, for example, a large cage is placed around the robot’s workspace that prevents humans from entering its vicinity. In the last few decades, efforts have been made to improve human-robot interaction. Often the movement of robots is characterized as not being smooth and clearly dividable into sub-movements. This makes their movement rather unpredictable for humans. So, there exists an opportunity to improve the motion generation of robots to enhance human-robot interaction. One interesting research direction is that of imitation learning. Here, human motions are recorded and demonstrated to the robot. Although the robot is able to reproduce such movements, it cannot be generalized to other situations. Therefore, a dynamical system approach is proposed where the recorded motions are embedded into the dynamics of the system. Shaping these nonlinear dynamics, according to recorded motions, allows for dynamical system to generalize beyond demonstration. As a result, the robot can generate motions of other situations not included in the recorded human demonstrations. In this dissertation, a Reservoir Computing approach is used to create a dynamical system in which such demonstrations are embedded. Reservoir Computing systems are Recurrent Neural Network-based approaches that are efficiently trained by considering only the training of the readout connections and retaining all other connections of such a network unchanged given their initial randomly chosen values. Although they have been used to embed periodic motions before, they were extended to embed discrete motions, or both. This work describes how such a motion pattern-generating system is built, investigates the nature of the underlying dynamics and evaluates their robustness in the face of perturbations. Additionally, a dynamical system approach to obstacle avoidance is proposed that is based on vector fields in the presence of repellers. This technique can be used to extend the motion abilities of the robot without need for changing the trained Motion Pattern Generator (MPG). Therefore, this approach can be applied in real-time on any system that generates a certain movement trajectory. Assume that the MPG system is implemented on an industrial robotic arm, similar to the ones used in a car factory. Even though the obstacle avoidance strategy presented is able to modify the generated motion of the robot’s gripper in such a way that it avoids obstacles, it does not guarantee that other parts of the robot cannot collide with a human. To prevent this, engineers have started to use advanced control algorithms that measure the amount of torque that is applied on the robot. This allows the robot to be aware of external perturbations. However, it turns out that, even with fast control loops, the adaptation to compensate for a sudden perturbation, is too slow to prevent high interaction forces. To reduce such forces, researchers started to use mechanical elements that are passively compliant (e.g., springs) and light-weight flexible materials to construct robots. Although such compliant robots are much safer and inherently energy efficient to use, their control becomes much harder. Most control approaches use model information about the robot (e.g., weight distribution and shape). However, when constructing a compliant robot it is hard to determine the dynamics of these materials. Therefore, a model-free adaptive control framework is proposed that assumes no prior knowledge about the robot. By interacting with the robot it learns an inverse robot model that is used as controller. The more it interacts, the better the control be- comes. Appropriately, this framework is called Inverse Modeling Adaptive (IMA) control framework. I have evaluated the IMA controller’s tracking ability on sev- eral tasks, investigating its model independence and stability. Furthermore, I have shown its fast learning ability and comparable performance to taskspecific designed controllers. Given both the MPG and IMA controllers, it is possible to improve the inter- actability of a compliant robot in a human-friendly environment. When the robot is to perform human-like motions for a large set of tasks, we need to demonstrate motion examples of all these tasks. However, biological research concerning the motion generation of animals and humans revealed that a limited set of motion patterns, called motion primitives, are modulated and combined to generate advanced motor/motion skills that humans and animals exhibit. Inspired by these interesting findings, I investigate if a single motion primitive indeed can be modulated to achieve a desired motion behavior. By some elementary experiments, where an MPG is controlled by an IMA controller, a proof of concept is presented. Furthermore, a general hierarchy is introduced that describes how a robot can be controlled in a biology-inspired manner. I also investigated how motion primitives can be combined to produce a desired motion. However, I was unable to get more advanced implementations to work. The results of some simple experiments are presented in the appendix. Another approach I investigated assumes that the primitives themselves are undefined. Instead, only a high-level description is given, which describes that every primitive on average should contribute equally, while still allowing for a single primitive to specialize in a part of the motion generation. Without defining the behavior of a primitive, only a set of untrained IMA controllers is used of which each will represent a single primitive. As a result of the high-level heuristic description, the task space is tiled into sub-regions in an unsupervised manner. Resulting in controllers that indeed represent a part of the motion generation. I have applied this Modular Architecture with Control Primitives (MACOP) on an inverse kinematic learning task and investigated the emerged primitives. Thanks to the tiling of the task space, it becomes possible to control redundant systems, because redundant solutions can be spread over several control primitives. Within each sub region of the task space, a specific control primitive is more accurate than in other regions allowing for the task complexity to be distributed over several less complex tasks. Finally, I extend the use of an IMA-controller, which is tracking controller, to the control of under-actuated systems. By using a sample-based planning algorithm it becomes possible to explore the system dynamics in which a path to a desired state can be planned. Afterwards, MACOP is used to incorporate feedback and to learn the necessary control commands corresponding to the planned state space trajectory, even if it contains errors. As a result, the under-actuated control of a cart pole system was achieved. Furthermore, I presented the concept of a simulation based control framework that allows the learning of the system dynamics, planning and feedback control iteratively and simultaneously

Robot Learning from Human Demonstration: Interpretation, Adaptation, and Interaction

Robot Learning from Demonstration (LfD) is a research area that focuses on how robots can learn new skills by observing how people perform various activities. As humans, we have a remarkable ability to imitate other human’s behaviors and adapt to new situations. Endowing robots with these critical capabilities is a significant but very challenging problem considering the complexity and variation of human activities in highly dynamic environments. This research focuses on how robots can learn new skills by interpreting human activities, adapting the learned skills to new situations, and naturally interacting with humans. This dissertation begins with a discussion of challenges in each of these three problems. A new unified representation approach is introduced to enable robots to simultaneously interpret the high-level semantic meanings and generalize the low-level trajectories of a broad range of human activities. An adaptive framework based on feature space decomposition is then presented for robots to not only reproduce skills, but also autonomously and efficiently adjust the learned skills to new environments that are significantly different from demonstrations. To achieve natural Human Robot Interaction (HRI), this dissertation presents a Recurrent Neural Network based deep perceptual control approach, which is capable of integrating multi-modal perception sequences with actions for robots to interact with humans in long-term tasks. Overall, by combining the above approaches, an autonomous system is created for robots to acquire important skills that can be applied to human-centered applications. Finally, this dissertation concludes with a discussion of future directions that could accelerate the upcoming technological revolution of robot learning from human demonstration

Learning probabilistic interaction models

We live in a multi-modal world; therefore it comes as no surprise that the human brain is tailored for the integration of multi-sensory input. Inspired by the human brain, the multi-sensory data is used in Artificial Intelligence (AI) for teaching different concepts to computers. Autonomous Agents (AAs) are AI systems that sense and act autonomously in complex dynamic environments. Such agents can build up Self-Awareness (SA) by describing their experiences through multi-sensorial information with appropriate models and correlating them incrementally with the currently perceived situation to continuously expand their knowledge. This thesis proposes methods to learn such awareness models for AAs. These models include SA and situational awareness models in order to perceive and understand itself (self variables) and its surrounding environment (external variables) at the same time. An agent is considered self-aware when it can dynamically observe and understand itself and its surrounding through different proprioceptive and exteroceptive sensors which facilitate learning and maintaining a contextual representation by processing the observed multi-sensorial data. We proposed a probabilistic framework for generative and descriptive dynamic models that can lead to a computationally efficient SA system. In general, generative models facilitate the prediction of future states while descriptive models enable to select the representation that best fits the current observation. The proposed framework employs a Probabilistic Graphical Models (PGMs) such as Dynamic Bayesian Networks (DBNs) that represent a set of variables and their conditional dependencies. Once we obtain this probabilistic representation, the latter allows the agent to model interactions between itself, as observed through proprioceptive sensors, and the environment, as observed through exteroceptive sensors. In order to develop an awareness system, not only an agent needs to recognize the normal states and perform predictions accordingly, but also it is necessary to detect the abnormal states with respect to its previously learned knowledge. Therefore, there is a need to measure anomalies or irregularities in an observed situation. In this case, the agent should be aware that an abnormality (i.e., a non-stationary condition) never experienced before, is currently present. Due to our specific way of representation, which makes it possible to model multi-sensorial data into a uniform interaction model, the proposed work not only improves predictions of future events but also can be potentially used to effectuate a transfer learning process where information related to the learned model can be moved and interpreted by another body

Probabilistic Graphical Models for Human Interaction Analysis

The objective of this thesis is to develop probabilistic graphical models for analyzing human interaction in meetings based on multimodel cues. We use meeting as a study case of human interactions since research shows that high complexity information is mostly exchanged through face-to-face interactions. Modeling human interaction provides several challenging research issues for the machine learning community. In meetings, each participant is a multimodal data stream. Modeling human interaction involves simultaneous recording and analysis of multiple multimodal streams. These streams may be asynchronous, have different frame rates, exhibit different stationarity properties, and carry complementary (or correlated) information. In this thesis, we developed three probabilistic graphical models for human interaction analysis. The proposed models use the ``probabilistic graphical model'' formalism, a formalism that exploits the conjoined capabilities of graph theory and probability theory to build complex models out of simpler pieces. We first introduce the multi-layer framework, in which the first layer models typical individual activity from low-level audio-visual features, and the second layer models the interactions. The two layers are linked by a set of posterior probability-based features. Next, we describe the team-player influence model, which learns the influence of interacting Markov chains within a team. The team-player influence model has a two-level structure: individual-level and group-level. Individual level models actions of each player, and the group-level models actions of the team as a whole. The influence of each player on the team is jointly learned with the rest of the model parameters in a principled manner using the Expectation-Maximization (EM) algorithm. Finally, we describe the semi-supervised adapted HMMs for unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supervised adapted Hidden Markov Model (HMM) framework, in which usual event models are first learned from a large amount of (commonly available) training data, while unusual event models are learned by Bayesian adaptation in an unsupervised manner