Vision-based foothold contact reasoning using curved surface patches

Reasoning about contacts between a legged robot's foot and the ground is a critical aspect of locomotion in natural terrains. This interaction becomes even more critical when the robot must move on rough surfaces. This paper presents a new visual contact analysis, based on curved patches that model local contact surfaces both on the sole of the robot's foot and in the terrain. The focus is on rigid, flat feet that represent the majority of the designs for current humanoids, but we also show how the introduced framework could be extended to other foot profiles, such as spherical feet. The footholds are localized visually in the environment's point cloud through a fast patch fitting process and a contact analysis between patches on the sole of the foot and in the surrounding environment. These patches aim to compose a spatial patch map for contact reasoning. We experimentally validate the introduced visionbased framework, using range data for rough terrain stepping demonstrations on the COMAN and WALK-MAN humanoids

RPBP: Rapid-prototyped remote-brain biped with 3D perception

This paper provides the design of a novel open-hardware mini-bipedal robot, named Rapid-Prototyped Remote-Brain BiPed (RPBP), that is developed to provide a low-cost and reliable platform for locomotion and perception research. The robot is made of customized 3D-printed material (ABS plastic) and electronics, and commercial Robotics Dynamixel MX-28 actuators, as well as visual RGB-D and IMU sensing systems. We show that the robot is able to perform some locomotion/visual-odometry tasks and it is easy to switch between different feet designs, providing also a novel Center-of-Pressure (CoP) sensing system, so that it can deal with various types of terrain. Moreover, we provide a description of its control and perception system architecture, as well as our opensource software packages that provide sensing and navigation tools for locomotion and visual odometry on the robot. Finally, we briefly discuss the transferability of some prototype research that has been done on the developed mini-biped, to half or fullsize humanoid robots, such as COMAN or WALK-MAN

ViT-A*: Legged Robot Path Planning using Vision Transformer A*

Legged robots, particularly quadrupeds, offer promising navigation capabilities, especially in scenarios requiring traversal over diverse terrains and obstacle avoidance. This paper addresses the challenge of enabling legged robots to navigate complex environments effectively through the integration of data-driven path-planning methods. We propose an approach that utilizes differentiable planners, allowing the learning of end-to-end global plans via a neural network for commanding quadruped robots. The approach leverages 2D maps and obstacle specifications as inputs to generate a global path. To enhance the functionality of the developed neural network-based path planner, we use Vision Transformers (ViT) for map pre-processing, to enable the effective handling of larger maps. Experimental evaluations on two real robotic quadrupeds (Boston Dynamics Spot and Unitree Go1) demonstrate the effectiveness and versatility of the proposed approach in generating reliable path plans

Reconfigurable and Agile Legged-Wheeled Robot Navigation in Cluttered Environments with Movable Obstacles

Legged and wheeled locomotion are two standard methods used by robots to perform navigation. Combining them to create a hybrid legged-wheeled locomotion results in increased speed, agility, and reconfigurability for the robot, allowing it to traverse a multitude of environments. The CENTAURO robot has these advantages, but they are accompanied by a higher-dimensional search space for formulating autonomous economical motion plans, especially in cluttered environments. In this article, we first review our previously presented legged-wheeled footprint reconfiguring global planner. We describe the two incremental prototypes, where the primary goal of the algorithms is to reduce the search space of possible footprints such that plans that expand the robot over the low-lying wide obstacles or narrow into passages can be computed with speed and efficiency. The planner also considers the cost of avoiding obstacles versus negotiating them by expanding over them. The second part of this article presents our new work on local obstacle pushing, which further increases the number of tight scenarios the planner can solve. The goal of the new local push-planner is to place any movable obstacle of unknown mass and inertial properties, obstructing the previously planned trajectory from our global planner, to a location devoid of obstruction. This is done while minimising the distance traveled by the robot, the distance the object is pushed, and its rotation caused by the push. Together, the local and global planners form a major part of the agile reconfigurable navigation suite for the legged-wheeled hybrid CENTAURO robot

Simulating Humans: Computer Graphics, Animation, and Control

People are all around us. They inhabit our home, workplace, entertainment, and environment. Their presence and actions are noted or ignored, enjoyed or disdained, analyzed or prescribed. The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object of interest and yet the most structurally complex. Their everyday movements are amazingly uid yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language

Design of a walking robot

Carnegie Mellon University's Autonomous Planetary Exploration Program (APEX) is currently building the Daedalus robot; a system capable of performing extended autonomous planetary exploration missions. Extended autonomy is an important capability because the continued exploration of the Moon, Mars and other solid bodies within the solar system will probably be carried out by autonomous robotic systems. There are a number of reasons for this - the most important of which are the high cost of placing a man in space, the high risk associated with human exploration and communication delays that make teleoperation infeasible. The Daedalus robot represents an evolutionary approach to robot mechanism design and software system architecture. Daedalus incorporates key features from a number of predecessor systems. Using previously proven technologies, the Apex project endeavors to encompass all of the capabilities necessary for robust planetary exploration. The Ambler, a six-legged walking machine was developed by CMU for demonstration of technologies required for planetary exploration. In its five years of life, the Ambler project brought major breakthroughs in various areas of robotic technology. Significant progress was made in: mechanism and control, by introducing a novel gait pattern (circulating gait) and use of orthogonal legs; perception, by developing sophisticated algorithms for map building; and planning, by developing and implementing the Task Control Architecture to coordinate tasks and control complex system functions. The APEX project is the successor of the Ambler project

The Mind-Body Problem and Its Solution (Second Edition)

Lays out the problem of sentience in a physical world and the solution based on the event ontology of Russell and Whitehead. The second edition includes the construction of physics from arrow diagrams, stemming from the discovery that the arrows of time form frequency ratios, which serve to define energy ratios and the quantum