Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with ...

https://journals.sagepub.com/doi/pdf/10.1177/1729881419839596

A review of mobile robots: Concepts, methods, theoretical framework, and applications

Publisher Summary This chapter discusses the psychology of risk: what risk is (if it is anything at all), how people think about it, what they feel about it, and what they do about it. The chapter describes the way psychologists think about risk: how they study it, what tasks they use, what factors they vary, and what models they build (or borrow) to describe risk-taking behavior. Technically, the word risk refers to situations in which a decision is made whose consequences depend on the outcomes of future events having known probabilities. Psychological studies of risky choice (it is the term used conventionally to refer to all but the most extreme instances of ignorance or ambiguity) fall into two groups. At one extreme are the studies run by mathematically inclined experimental psychologists in which subjects make decisions about gambles described in terms of amounts and probabilities. At the other extreme are studies run by personality psychologists, who are mostly interested in individual differences in risk taking. A theory of risky choice is presented in the chapter that attempts to meld the strengths of both approaches. Empirically and methodologically it is tied to the experimental approach to risky choice. But theoretically it is more strongly tied to motivational approaches.

[Advances in Experimental Social Psychology] Advances in Experimental Social Psychology Volume 20 Volume 20 || Between Hope and Fear: The Psychology of Risk

This paper addresses a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim) to bridge the gap between simulation and real-world, and automate a flexible object manipulation task. This strategy consists of three steps: (1) using the rough environment with the estimated models to develop the methods to complete the manipulation task in the simulation; (2) applying the methods from simulation to real-world and comparing their performance; (3) updating the models and methods in simulation based on the differences between the real world and the simulation. The Plug Task from the 2015 DARPA Robotics Challenge Finals is chosen to evaluate our Sim2Real2Sim strategy. A new identification approach for building the model of the linear flexible objects is derived from real-world to simulation. The automation of the DRC plug task in both simulation and real-world proves the success of the Sim2Real2Sim strategy. Numerical experiments are implemented to validate the simulated model.

Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation

Robot performance measures are essential tools for quantifying the ability to execute manipulation tasks. Typically, these measures focus on the system's geometric structure and how it impacts the transformation from joint to Cartesian space. In this paper, we propose a new method to evaluate the robot's performance that considers both the system's geometric structure and the presence of obstacles close to or in contact with the robot. This method reduces the manipulator's joint velocity limits by deforming the manipulability polytope to account for obstacles. These constraints are then propagated throughout the chain to get a more representative measure of the end effector's velocity capabilities. The proposed method leads to improved understanding of the robot's capacities in a constrained environment.

Evaluating Robot Manipulability in Constrained Environments by Velocity Polytope Reduction

This paper addresses a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim) to bridge the gap between simulation and real-world, and automate a flexible object manipulation task. This strategy consists of three steps: (1) using the rough environment with the estimated models to develop the methods to complete the manipulation task in the simulation; (2) applying the methods from simulation to realworld and comparing their performance; (3) updating the models and methods in simulation based on the differences between the real world and the simulation. The Plug Task from the 2015 DARPA Robotics Challenge Finals is chosen to evaluate our Sim2Real2Sim strategy. A new identification approach for building the model of the linear flexible objects is derived from real-world to simulation. The automation of the DRC plug task in both simulation and real-world proves the success of the Sim2Real2Sim strategy. Numerical experiments are implemented to validate the simulated model.

There is a significant amount of synergy between virtual reality (VR) and the field of robotics. However, it has only been in approximately the past five years that commercial immersive VR devices have been available to developers. This new availability has led to a rapid increase in research using VR devices in the field of robotics, especially in the development of VR interfaces for operating robots. In this paper, we present a systematic review on VR interfaces for robot operation that utilize commercially available immersive VR devices. A total of 41 papers published between 2016–2020 were collected for review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Papers are discussed and categorized into five categories: (1) Visualization, which focuses on displaying data or information to operators; (2) Robot Control and Planning, which focuses on connecting human input or movement to robot movement; (3) Interaction, which focuses on the development of new interaction techniques and/or identifying best interaction practices; (4) Usability, which focuses on user experiences of VR interfaces; and (5) Infrastructure, which focuses on system architectures or software to support connecting VR and robots for interface development. Additionally, we provide future directions to continue development in VR interfaces for operating robots.

A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots

This research is aimed at investigating the interaction for a bipedal humanoid robot during locomotion on deformable terrains. The knowledge of deformable terrain properties has major implications in modelling the robot walking dynamics, which is the key to achieve stable gait patterns. We propose an in-situ terrain classification and estimation method in this paper to provide robots with such terrain properties. In particular, we introduce our method for classifying known terrains using recurrent neural networks (RNNs) and estimating the unknown terrain’s stiffness adopting the Bernstein-Goriatchkin model. We present our experimental procedures and analysis of the resulting data using NASA’s humanoid robot, Valkyrie, while it is performing a set of prespecified motions on different terrains, including gym foam mat, wood mulch, rubber mulch, and sand. It is concluded that using RNNs, we can efficiently classify the terrain types. For unknown terrains, our approach can estimate the terrain’s stiffness that demonstrates support forces under deformation.

In-situ Terrain Classification and Estimation for NASA’s Humanoid Robot Valkyrie

In the last decade, there have been great advancements in virtual reality (VR) resulting in its availability for everyday consumers. As VR becomes more ubiquitous, there is an opportunity to utilize this technology to create intuitive operator interfaces for interaction with complex dynamic systems, such as humanoid robots. As evidenced in the DARPA Robotics Challenge (DRC), current interfaces for humanoids primarily use a standard computer setup with monitor, keyboard, and mouse requiring operators to process 3D data with 2D devices. And although these interfaces can be very capable in operating a robot, they are often complex and require expert operators as well as extensive training. However, this paradigm can be changed with VR by allowing operators to visualize and interact with 3D data in a 3D environment, allowing for a more natural interaction. In this paper, we present our work on converting a typical interface to a virtual reality interface for NASA's humanoid robot, Valkyrie. We compare our standard computer interface and our VR interface for Valkyrie, as well as the shared control planners and system architecture that make our interfaces possible. The goal of this work is to better understand the utility of virtual reality interfaces and how they can be employed in human-supervised robot applications so that we may move towards more intuitive and easy-to-use interfaces for control and interaction.

Human-Humanoid Robot Interaction through Virtual Reality Interfaces

Two major questions humanoid robots need to solve for manipulation tasks are whether they need to take steps and where to take steps to. In this paper, we introduce the formulation of a powerful inverse kinematics (IK) engine which can help to answer these questions. In the engine, the IK problem is formulated as an optimization problem. After configuring appropriate costs and constraints, the IK engine can compute a solution in which the robot is allowed to reposition its feet if necessary for meeting the task requirements. When the answers of these two questions are found, an integrated planning algorithm involving an IK solver, footstep planning and whole body manipulation motion planning is proposed. An object pickup scenario using the NASA-JSC Valkyrie robot is also provided in this paper to demonstrate the performance of the planning algorithm.

Task-oriented planning algorithm for humanoid robots based on a foot repositionable inverse kinematics engine

This paper introduces an anytime synthesized motion planning algorithm for humanoid robots unifying locomotion and manipulation planning. It generates an entire set of motions to finish specific tasks in an environment containing obstacles by exploiting a powerful inverse kinematics (IK) engine. The IK engine can compute solutions allowing the robot to reposition its feet for meeting the task requirements. The presented planning algorithm has two primary beneficial capabilities. First, it is capable of generating a motion plan to complete a task handling multiple ordered or unordered actions. Second, it produces an initial solution very quickly, and then searches for the opportunity to improve the the solution during execution. The performance of the proposed algorithm is evaluated on the NASA-JSC Valkyrie humanoid robot by demonstrating an object pick up task in simulation and a box pick-and-place task in the real world.

Murphy Wonsick

Papers

A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots

In-situ Terrain Classification and Estimation for NASA’s Humanoid Robot Valkyrie

Human-Humanoid Robot Interaction through Virtual Reality Interfaces

Task-oriented planning algorithm for humanoid robots based on a foot repositionable inverse kinematics engine

Anytime multi-task motion planning for humanoid robots