This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Modelling and Control of Robot Manipulators

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics.

https://journals.sagepub.com/doi/pdf/10.1177/0278364918770733

The limits and potentials of deep learning for robotics

To plan dynamic, whole-body motions for robots, one conventionally faces the choice between a complex, full-body dynamic model containing every link and actuator of the robot, or a highly simplified model of the robot as a point mass. In this paper we explore a powerful middle ground between these extremes. We exploit the fact that while the full dynamics of humanoid robots are complicated, their centroidal dynamics (the evolution of the angular momentum and the center of mass (COM) position) are much simpler. By treating the dynamics of the robot in centroidal form and directly optimizing the joint trajectories for the actuated degrees of freedom, we arrive at a method that enjoys simpler dynamics, while still having the expressiveness required to handle kinematic constraints such as collision avoidance or reaching to a target. We further require that the robot's COM and angular momentum as computed from the joint trajectories match those given by the centroidal dynamics. This ensures that the dynamics considered by our optimization are equivalent to the full dynamics of the robot, provided that the robot's actuators can supply sufficient torque. We demonstrate that this algorithm is capable of generating highly-dynamic motion plans with examples of a humanoid robot negotiating obstacle course elements and gait optimization for a quadrupedal robot. Additionally, we show that we can plan without pre-specifying the contact sequence by exploiting the complementarity conditions between contact forces and contact distance.

/pdf/whole-body-motion-planning-with-centroidal-dynamics-and-full-u337p82it7.pdf

Whole-body motion planning with centroidal dynamics and full kinematics

A wide variety of tactile (touch) sensors exist today for robotics and related applications. They make use of various transduction methods, smart materials and engineered structures, complex electronics, and sophisticated data processing. While highly useful in themselves, effective utilization of tactile sensors in robotics applications has been slow to come and largely remains elusive today. This paper surveys the state of the art and the research issues in this area, with the emphasis on effective utilization of tactile sensors in robotic systems. One specific with the use of tactile sensing in robotics is that the sensors have to be spread along the robot body, the way the human skin is-thus dictating varied 3-D spatio-temporal requirements, decentralized and distributed control, and handling of multiple simultaneous tactile contacts. Satisfying these requirements pose challenges to making tactile sensor modality a reality. Overcoming these challenges requires dealing with issues such as sensors placement, electronic/mechanical hardware, methods to access and acquire signals, automatic calibration techniques, and algorithms to process and interpret sensing data in real time. We survey this field from a system perspective, recognizing the fact that the system performance tends to depend on how its various components are put together. It is hoped that the survey will be of use to practitioners designing tactile sensing hardware (whole-body or large-patch sensor coverage), and to researchers working on cognitive robotics involving tactile sensing.

Directions Toward Effective Utilization of Tactile Skin: A Review

PROBLEM TO BE SOLVED: To provide a system and a method for calibrating a rotary absolute position sensor. SOLUTION: This system includes a rotary device, a rotary absolute position (RAP) sensor generating encoded pairs of voltage signals describing positional data of the rotary device, a host machine, and an algorithm. The algorithm includes steps for: measuring the rotational position as encoded pairs of voltage signals; linearly-mapping an ellipse defined by the encoded pairs to thereby calculate the calibration parameters; and calculating an absolute position of the rotary device by using the calibration parameters. The calibration parameters include a positive definite matrix and a center point of the ellipse. The voltage signals may include an encoded sine and cosine of a rotary angle of the rotary device. COPYRIGHT: (C)2011,JPO&INPIT

System and method for calibrating rotary absolute position sensor

Ein Steuerungssystem zum Erreichen eines Hochgeschwindigkeitsdrehmoments fur ein Gelenk eines Roboters umfasst eine gedruckte Leiterplattenanordnung (PCBA) mit einem gemeinsam angeordneten Gelenkprozessor und einem Hochgeschwindigkeitskommunikationsbus. Die PCBA kann auch ein Gleichrichter/Wechselrichter-Modul (PIM) und eine lokale Sensoraufbereitungselektronik (SCE) zur Verarbeitung von Sensordaten von einem oder mehreren Motorpositionssensoren umfassen. Die Drehmomentregelung eines Motors des Gelenks wird uber die PCBA als ein Hochgeschwindigkeitsdrehmomentkreis bereitgestellt. Jeder Gelenkprozessor kann in dem Robotergelenk, das gesteuert wird, eingebettet sein oder zusammen damit angeordnet sein. Die gemeinsame Anordnung des Gelenkprozessors, des PIM und des Hochgeschwindigkeitsbusses kann die Unempfindlichkeit des Steuerungssystems gegenuber Rauschen verbessern und die lokale Verarbeitung von Sensordaten vom Gelenkmotor auf der Gelenkebene kann eine Busverkabelung an und von jedem Steuerungsknoten minimieren. Der Gelenkprozessor kann ein im Feld programmierbares Gatearray (FPGA) umfassen.

Integriertes Hochgeschwindigkeitsdrehmomentregelsystem für ein Robotergelenk

Ein Robotersystem zum Ausfuhren einer autonomen Aufgabe umfasst einen humanoiden Roboter, der eine Vielzahl von nachgiebigen Robotergelenken, Stellgliedern und anderen integrierten Systemeinrichtungen aufweist, die in Ansprechen auf Steuerungsdaten von verschiedenen Steuerungspunkten steuerbar sind, und der Sensoren aufweist, um Ruckkopplungsdaten bei den Steuerungspunkten zu messen. Das System umfasst ein verteiltes Steuerungsgerust (DCF) mit mehreren Ebenen zum Steuern der integrierten Systemkomponenten uber mehrere Hochgeschwindigkeitskommunikationsnetzwerke hinweg. Das DCF weist eine Vielzahl von ersten Controllern, die jeweils in eine jeweilige der integrierten Systemkomponenten eingebettet sind, z. B. die Robotergelenke, einen zweiten Controller, der die Komponenten mit Hilfe der ersten Controller koordiniert, und einen dritten Controller auf, um ein Signal zu ubertragen, das dem zweiten Controller das Ausfuhren der autonomen Aufgabe befiehlt. Das DCF zentralisiert alle Steuerungsdaten und Ruckkopplungsdaten auf virtuelle Weise an einem einzigen Ort, um eine Steuerung des Roboters uber die mehreren Kommunikationsnetzwerke hinweg zu erleichtern.

Gerüst und Verfahren zum Steuern eines Robotersystems unter Verwendung eines verteilten Rechnernetzwerks

Denoising diffusion models trained at web-scale have revolutionized image generation. The application of these tools to engineering design is an intriguing possibility, but is currently limited by their inability to parse and enforce concrete engineering constraints. In this paper, we take a step towards this goal by proposing physics-based guidance, which enables optimization of a performance metric (as predicted by a surrogate model) during the generation process. As a proof-of-concept, we add drag guidance to Stable Diffusion, which allows this tool to generate images of novel vehicles while simultaneously minimizing their predicted drag coefficients.

Drag-guided diffusion models for vehicle image generation

Applying an interior-point method to the central-path conditions is a widely used approach for solving quadratic programs. Reformulating these conditions in the log-domain is a natural variation on this approach that to our knowledge is previously unstudied. In this paper, we analyze log-domain interior-point methods and prove their polynomial-time convergence. We also prove that they are approximated by classical barrier methods in a precise sense and provide simple computational experiments illustrating their superior performance. 

/pdf/log-domain-interior-point-methods-for-convex-quadratic-15d4gq1g.pdf

Frank Noble Permenter

Papers

System and method for calibrating rotary absolute position sensor

Integriertes Hochgeschwindigkeitsdrehmomentregelsystem für ein Robotergelenk

Gerüst und Verfahren zum Steuern eines Robotersystems unter Verwendung eines verteilten Rechnernetzwerks

Drag-guided diffusion models for vehicle image generation

Log-domain interior-point methods for convex quadratic programming