Reaction Null Space of a multibody system with applications in robotics

This paper provides an overview of implementation examples based on the Reaction Null Space formalism, developed initially to tackle the problem of satellite-base disturbance of a free-floating space robot, when the robot arm is activated. The method has been applied throughout the years to other unfixed-base systems, e.g. flexible-base and macro/mini robot systems, as well as to the balance control problem of humanoid robots. The paper also includes most recent results about complete dynamical decoupling of the end-link of a fixed-base robot, wherein the end-link is regarded as the unfixed-base. This interpretation is shown to be useful with regard to motion/force control scenarios. Respective implementation results are provided

Scaled Autonomy for Networked Humanoids

Humanoid robots have been developed with the intention of aiding in environments designed for humans. As such, the control of humanoid morphology and effectiveness of human robot interaction form the two principal research issues for deploying these robots in the real world. In this thesis work, the issue of humanoid control is coupled with human robot interaction under the framework of scaled autonomy, where the human and robot exchange levels of control depending on the environment and task at hand. This scaled autonomy is approached with control algorithms for reactive stabilization of human commands and planned trajectories that encode semantically meaningful motion preferences in a sequential convex optimization framework. The control and planning algorithms have been extensively tested in the field for robustness and system verification. The RoboCup competition provides a benchmark competition for autonomous agents that are trained with a human supervisor. The kid-sized and adult-sized humanoid robots coordinate over a noisy network in a known environment with adversarial opponents, and the software and routines in this work allowed for five consecutive championships. Furthermore, the motion planning and user interfaces developed in the work have been tested in the noisy network of the DARPA Robotics Challenge (DRC) Trials and Finals in an unknown environment. Overall, the ability to extend simplified locomotion models to aid in semi-autonomous manipulation allows untrained humans to operate complex, high dimensional robots. This represents another step in the path to deploying humanoids in the real world, based on the low dimensional motion abstractions and proven performance in real world tasks like RoboCup and the DRC

A Comparative Experimental Study of Multi-Tasking Tracking and Interaction Control on a Torque-Controlled Humanoid Robot

Multi-tasking control exploits kinematic redundancy of robots to attain several control objectives at the same time. To properly coordinate the subtasks according to their importance, they are usually stacked into a prioritized hierarchy. In this work, two passivity-based multi-tasking control strategies developed in our recent work that feature strict prioritization and mathematically proved stability properties, are experimentally compared with a state-of-the-art method using feedback linearization on a torque-controlled humanoid robot. The conducted experimental study aims at providing insights into the practical properties of the controllers in real-world scenarios whence the robot has to execute a mixture of trajectory tracking and physical interaction tasks

Bringing a Humanoid Robot Closer to Human Versatility : Hard Realtime Software Architecture and Deep Learning Based Tactile Sensing

For centuries, it has been a vision of man to create humanoid robots, i.e., machines that not only resemble the shape of the human body, but have similar capabilities, especially in dextrously manipulating their environment. But only in recent years it has been possible to build actual humanoid robots with many degrees of freedom (DOF) and equipped with torque controlled joints, which are a prerequisite for sensitively acting in the world. In this thesis, we extend DLR's advanced mobile torque controlled humanoid robot Agile Justin into two important directions to get closer to human versatility. First, we enable Agile Justin, which was originally built as a research platform for dextrous mobile manipulation, to also be able to execute complex dynamic manipulation tasks. We demonstrate this with the challenging task of catching up to two simultaneously thrown balls with its hands. Second, we equip Agile Justin with highly developed and deep learning based tactile sensing capabilities that are critical for dextrous fine manipulation. We demonstrate its tactile capabilities with the delicate task of identifying an objects material simply by gently sweeping with a fingertip over its surface. Key for the realization of complex dynamic manipulation tasks is a software framework that allows for a component based system architecture to cope with the complexity and parallel and distributed computational demands of deep sensor-perception-planning-action loops -- but under tight timing constraints. This thesis presents the communication layer of our aRDx (agile robot development -- next generation) software framework that provides hard realtime determinism and optimal transport of data packets with zero-copy for intra- and inter-process and copy-once for distributed communication. In the implementation of the challenging ball catching application on Agile Justin, we take full advantage of aRDx's performance and advanced features like channel synchronization. Besides developing the challenging visual ball tracking using only onboard sensing while everything is moving and the automatic and self-contained calibration procedure to provide the necessary precision, the major contribution is the unified generation of the reaching motion for the arms. The catch point selection, motion planning and the joint interpolation steps are subsumed in one nonlinear constrained optimization problem which is solved in realtime and allows for the realization of different catch behaviors. For the highly sensitive task of tactile material classification with a flexible pressure-sensitive skin on Agile Justin's fingertip, we present our deep convolutional network architecture TactNet-II. The input is the raw 16000 dimensional complex and noisy spatio-temporal tactile signal generated when sweeping over an object's surface. For comparison, we perform a thorough human performance experiment with 15 subjects which shows that Agile Justin reaches superhuman performance in the high-level material classification task (What material id?), as well as in the low-level material differentiation task (Are two materials the same?). To increase the sample efficiency of TactNet-II, we adapt state of the art deep end-to-end transfer learning to tactile material classification leading to an up to 15 fold reduction in the number of training samples needed. The presented methods led to six publication awards and award finalists and international media coverage but also worked robustly at many trade fairs and lab demos

A Quadratic Programming Approach to Quasi-Static Whole-Body Manipulation

This paper introduces a local motion planning method for robotic systems with manipulating limbs, moving bases (legged or wheeled), and stance stability constraints arising from the presence of gravity. We formulate the problem of selecting local motions as a linearly constrained quadratic program (QP), that can be solved efficiently. The solution to this QP is a tuple of locally optimal joint velocities. By using these velocities to step towards a goal, both a path and an inverse-kinematic solution to the goal are obtained. This formulation can be used directly for real-time control, or as a local motion planner to connect waypoints. This method is particularly useful for high-degree-of-freedom mobile robotic systems, as the QP solution scales well with the number of joints. We also show how a number of practically important geometric constraints (collision avoidance, mechanism self-collision avoidance, gaze direction, etc.) can be readily incorporated into either the constraint or objective parts of the formulation. Additionally, motion of the base, a particular joint, or a particular link can be encouraged/discouraged as desired. We summarize the important kinematic variables of the formulation, including the stance Jacobian, the reach Jacobian, and a center of mass Jacobian. The method is easily extended to provide sparse solutions, where the fewest number of joints are moved, by iteration using Tibshirani’s method to accommodate an l_1 regularizer. The approach is validated and demonstrated on SURROGATE, a mobile robot with a TALON base, a 7 DOF serial-revolute torso, and two 7 DOF modular arms developed at JPL/Caltech

Humanoid Robot Soccer Locomotion and Kick Dynamics: Open Loop Walking, Kicking and Morphing into Special Motions on the Nao Robot

Striker speed and accuracy in the RoboCup (SPL) international robot soccer league is becoming increasingly important as the level of play rises. Competition around the ball is now decided in a matter of seconds. Therefore, eliminating any wasted actions or motions is crucial when attempting to kick the ball. It is common to see a discontinuity between walking and kicking where a robot will return to an initial pose in preparation for the kick action. In this thesis we explore the removal of this behaviour by developing a transition gait that morphs the walk directly into the kick back swing pose. The solution presented here is targeted towards the use of the Aldebaran walk for the Nao robot. The solution we develop involves the design of a central pattern generator to allow for controlled steps with realtime accuracy, and a phase locked loop method to synchronise with the Aldebaran walk so that precise step length control can be activated when required. An open loop trajectory mapping approach is taken to the walk that is stabilized statically through the use of a phase varying joint holding torque technique. We also examine the basic princples of open loop walking, focussing on the commonly overlooked frontal plane motion. The act of kicking itself is explored both analytically and empirically, and solutions are provided that are versatile and powerful. Included as an appendix, the broader matter of striker behaviour (process of goal scoring) is reviewed and we present a velocity control algorithm that is very accurate and efficient in terms of speed of execution

Toward Robots with Peripersonal Space Representation for Adaptive Behaviors

The abilities to adapt and act autonomously in an unstructured and human-oriented environment are necessarily vital for the next generation of robots, which aim to safely cooperate with humans. While this adaptability is natural and feasible for humans, it is still very complex and challenging for robots. Observations and findings from psychology and neuroscience in respect to the development of the human sensorimotor system can inform the development of novel approaches to adaptive robotics. Among these is the formation of the representation of space closely surrounding the body, the Peripersonal Space (PPS) , from multisensory sources like vision, hearing, touch and proprioception, which helps to facilitate human activities within their surroundings. Taking inspiration from the virtual safety margin formed by the PPS representation in humans, this thesis first constructs an equivalent model of the safety zone for each body part of the iCub humanoid robot. This PPS layer serves as a distributed collision predictor, which translates visually detected objects approaching a robot\u2019s body parts (e.g., arm, hand) into the probabilities of a collision between those objects and body parts. This leads to adaptive avoidance behaviors in the robot via an optimization-based reactive controller. Notably, this visual reactive control pipeline can also seamlessly incorporate tactile input to guarantee safety in both pre- and post-collision phases in physical Human-Robot Interaction (pHRI). Concurrently, the controller is also able to take into account multiple targets (of manipulation reaching tasks) generated by a multiple Cartesian point planner. All components, namely the PPS, the multi-target motion planner (for manipulation reaching tasks), the reaching-with-avoidance controller and the humancentred visual perception, are combined harmoniously to form a hybrid control framework designed to provide safety for robots\u2019 interactions in a cluttered environment shared with human partners. Later, motivated by the development of manipulation skills in infants, in which the multisensory integration is thought to play an important role, a learning framework is proposed to allow a robot to learn the processes of forming sensory representations, namely visuomotor and visuotactile, from their own motor activities in the environment. Both multisensory integration models are constructed with Deep Neural Networks (DNNs) in such a way that their outputs are represented in motor space to facilitate the robot\u2019s subsequent actions

An Architecture for Online Affordance-based Perception and Whole-body Planning

The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot's sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule