No Arabic abstract
In this paper, we present a toolchain to design, execute, and verify robot behaviors. The toolchain follows the guidelines defined by the EU H2020 project RobMoSys and encodes the robot deliberation as a Behavior Tree (BT), a directed tree where the internal nodes model behavior composition and leaf nodes model action or measurement operations. Such leaf nodes take the form of a statechart (SC), which runs in separate threads, whose states perform basic arithmetic operations and send commands to the robot. The toolchain provides the ability to define a runtime monitor for a given system specification that warns the user whenever a given specification is violated. We validated the toolchain in a simulated experiment that we made reproducible in an OS-virtualization environment.
An appropriate user interface to collect human demonstration data for deformable object manipulation has been mostly overlooked in the literature. We present an interaction design for demonstrating cloth folding to robots. Users choose pick and place points on the cloth and can preview a visualization of a simulated cloth before real-robot execution. Two interfaces are proposed: A 2D display-and-mouse interface where points are placed by clicking on an image of the cloth, and a 3D Augmented Reality interface where the chosen points are placed by hand gestures. We conduct a user study with 18 participants, in which each user completed two sequential folds to achieve a cloth goal shape. Results show that while both interfaces were acceptable, the 3D interface was found to be more suitable for understanding the task, and the 2D interface suitable for repetition. Results also found that fold previews improve three key metrics: task efficiency, the ability to predict the final shape of the cloth and overall user satisfaction.
For the majority of tasks performed by traditional serial robot arms, such as bin picking or pick and place, only two or three degrees of freedom (DOF) are required for motion; however, by augmenting the number of degrees of freedom, further dexterity of robot arms for multiple tasks can be achieved. Instead of increasing the number of joints of a robot to improve flexibility and adaptation, which increases control complexity, weight, and cost of the overall system, malleable robots utilise a variable stiffness link between joints allowing the relative positioning of the revolute pairs at each end of the link to vary, thus enabling a low DOF serial robot to adapt across tasks by varying its workspace. In this paper, we present the design and prototyping of a 2-DOF malleable robot, calculate the general equation of its workspace using a parameterisation based on distance geometry---suitable for robot arms of variable topology, and characterise the workspace categories that the end effector of the robot can trace via reconfiguration. Through the design and construction of the malleable robot we explore design considerations, and demonstrate the viability of the overall concept. By using motion tracking on the physical robot, we show examples of the infinite number of workspaces that the introduced 2-DOF malleable robot can achieve.
This paper presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and robust for humanoids. We proposed novel approaches to introduce human bias, i.e. motion capture data and a special Multi-Expert network structure. We used the Multi-Expert network structure to smoothly blend behavioral features, and used the augmented reward design for the task and imitation rewards. Our reward design is composable, tunable, and explainable by using fundamental concepts from conventional humanoid control. We rigorously validated and benchmarked the learning framework which consistently produced robust locomotion behaviors in various test scenarios. Further, we demonstrated the capability of learning robust and versatile policies in the presence of disturbances, such as terrain irregularities and external pushes.
Aerial autonomous machines (Drones) has a plethora of promising applications and use cases. While the popularity of these autonomous machines continues to grow, there are many challenges, such as endurance and agility, that could hinder the practical deployment of these machines. The closed-loop control frequency must be high to achieve high agility. However, given the resource-constrained nature of the aerial robot, achieving high control loop frequency is hugely challenging and requires careful co-design of algorithm and onboard computer. Such an effort requires infrastructures that bridge various domains, namely robotics, machine learning, and system architecture design. To that end, we present AutoSoC, a framework for co-designing algorithms as well as hardware accelerator systems for end-to-end learning-based aerial autonomous machines. We demonstrate the efficacy of the framework by training an obstacle avoidance algorithm for aerial robots to navigate in a densely cluttered environment. For the best performing algorithm, our framework generates various accelerator design candidates with varying performance, area, and power consumption. The framework also runs the ASIC flow of place and route and generates a layout of the floor-planed accelerator, which can be used to tape-out the final hardware chip.
One of the current challenges of Information Systems is to ensure semi-structured data transmission, such as multimedia data, in a distributed and pervasive environment. Information Sytems must then guarantee users a quality of service ensuring data accessibility whatever the hardware and network conditions may be. They must also guarantee information coherence and particularly intelligibility that imposes a personalization of the service. Within this framework, we propose a design method based on original models of multimedia applications and quality of service. We also define a supervision platform Kalinahia using a user centered heuristic allowing us to define at any moment which configuration of software components constitutes the best answers to users wishes in terms of service.