Demonstration-free contextualized probabilistic movement primitives, further enhanced with obstacle avoidance

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Movement Primitives (MPs) have been widely used over the last years for learning robot motion tasks with direct Policy Search (PS) reinforcement learning. Among them, Probabilistic Movement Primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, which benefits from the properties of probability operations. However, the generation of such ProMPs requires a set of demonstrations to capture motion variability. Additionally, using context variables to modify trajectories coded as MPs is a popular approach nowadays in order to adapt motion to environmental variables. This paper proposes a contextual representation of ProMPs that allows for an easy adaptation to changing situations through context variables, by reparametrizing motion with them. Moreover, we propose a way of initializing contextual trajectories without the need of real robot demonstrations, by setting an initial position, a final position, and a number of trajectory interest points, where the contextual variables are evaluated. The parametrizations obtained show to be accurate while relieving the user from the need of performing costly computations such as conditioning. Additionally, using this contextual representation, we propose a simple yet effective quadratic optimization-based obstacle avoidance method for ProMPs. Experiments in simulation and on a real robot show the promise of the approach.Peer ReviewedPostprint (author's final draft

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

Behavioral Repertoire via Generative Adversarial Policy Networks

Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment may require using different behaviors to achieve the same effect, for instance to reach and grasp an object in changing clutter. An emerging paradigm addressing this robustness issue is to learn a diverse set of successful behaviors for a given task, from which a robot can select the most suitable policy when faced with a new environment. In this paper, we explore a novel realization of this vision by learning a generative model over policies. Rather than learning a single policy, or a small fixed repertoire, our generative model for policies compactly encodes an unbounded number of policies and allows novel controller variants to be sampled. Leveraging our generative policy network, a robot can sample novel behaviors until it finds one that works for a new environment. We demonstrate this idea with an application of robust ball-throwing in the presence of obstacles. We show that this approach achieves a greater diversity of behaviors than an existing evolutionary approach, while maintaining good efficacy of sampled behaviors, allowing a Baxter robot to hit targets more often when ball throwing in the presence of obstacles.Comment: In Proceedings of 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pages 320 - 32

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces

Queißer J, Steil JJ. Bootstrapping of parameterized skills through hybrid optimization in task and policy spaces. Frontiers in Robotics and AI. 2018;5:49.Modern robotic applications create high demands on adaptation of actions with respect to variance in a given task. Reinforcement learning is able to optimize for these changing conditions, but relearning from scratch is hardly feasible due to the high number of required rollouts. We propose a parameterized skill that generalizes to new actions for changing task parameters, which is encoded as a meta-learner that provides parameters for task-specific dynamic motion primitives. Our work shows that utilizing parameterized skills for initialization of the optimization process leads to a more effective incremental task learning. In addition, we introduce a hybrid optimization method that combines a fast coarse optimization on a manifold of policy parameters with a fine grained parameter search in the unrestricted space of actions. The proposed algorithm reduces the number of required rollouts for adaptation to new task conditions. Application in illustrative toy scenarios, for a 10-DOF planar arm, and a humanoid robot point reaching task validate the approach

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

Migration from Teleoperation to Autonomy via Modular Sensor and Mobility Bricks

In this thesis, the teleoperated communications of a Remotec ANDROS robot have been reverse engineered. This research has used the information acquired through the reverse engineering process to enhance the teleoperation and add intelligence to the initially automated robot. The main contribution of this thesis is the implementation of the mobility brick paradigm, which enables autonomous operations, using the commercial teleoperated ANDROS platform. The brick paradigm is a generalized architecture for a modular approach to robotics. This architecture and the contribution of this thesis are a paradigm shift from the proprietary commercial models that exist today. The modular system of sensor bricks integrates the transformed mobility platform and defines it as a mobility brick. In the wall following application implemented in this work, the mobile robotic system acquires intelligence using the range sensor brick. This application illustrates a way to alleviate the burden on the human operator and delegate certain tasks to the robot. Wall following is one among several examples of giving a degree of autonomy to an essentially teleoperated robot through the Sensor Brick System. Indeed once the proprietary robot has been altered into a mobility brick; the possibilities for autonomy are numerous and vary with different sensor bricks. The autonomous system implemented is not a fixed-application robot but rather a non-specific autonomy capable platform. Meanwhile the native controller and the computer-interfaced teleoperation are still available when necessary. Rather than trading off by switching from teleoperation to autonomy, this system provides the flexibility to switch between the two at the operator’s command. The contributions of this thesis reside in the reverse engineering of the original robot, its upgrade to a computer-interfaced teleoperated system, the mobility brick paradigm and the addition of autonomy capabilities. The application of a robot autonomously following a wall is subsequently implemented, tested and analyzed in this work. The analysis provides the programmer with information on controlling the robot and launching the autonomous function. The results are conclusive and open up the possibilities for a variety of autonomous applications for mobility platforms using modular sensor bricks

DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience

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

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described