Abstract

In this paper, we present an image and model dataset of the real-life objects from the Yale-CMU-Berkeley Object Set, which is specifically designed for benchmarking in manipulation research. For each object, the dataset presents 600 high-resolution RGB images, 600 RGB-D images and five sets of textured three-dimensional geometric models. Segmentation masks and calibration information for each image are also provided. These data are acquired using the BigBIRD Object Scanning Rig and Google Scanners. Together with the dataset, Python scripts and a Robot Operating System node are provided to download the data, generate point clouds and create Unified Robot Description Files. The dataset is also supported by our website, www.ycbbenchmarks.org , which serves as a portal for publishing and discussing test results along with proposing task protocols and benchmarks.

No abstract

Mobile robots often find themselves in a situation where they must find a trajectory to another position in their environment, subject to constraints posed by obstacles and the capabilities of the robot itsew. This is the problem of planning a path through a continuow domain, for which several approaches have been developed. Each has some limitations however, including requiring state discretuations, steep efficiency us. accuracy tmdeoffs, or the difficulty of adding interleaved execution. Rapidly-Exploring Random Trees (RRTs) are a recently developed representation on which fast continuow domain path planners can be based. In this work, we build a path planning system based on RRTs that interleaves planning and execution, first evaluating it in simulation and then applying it to physical robots. Our planning algorithm, ERRT (execution eztended RRT), introduces two novel eztensions of previous RRT work, the waypoint cache and adaptive cost penalty search, which improve replanning eficiency and the quality of generated paths. ERRT is success&lly applied to a real-time multi-robot system. Results demonstrate that ERRT is significantly more efficient for replanning than a basic RRT planner, performing wmpetitively with or better than existing heuristic and reactive real-time path planning approaches. ERRT is a significant step forward with the potential for making path planning common on real robots, even in challenging continuous, highly dynamic domains. 0-7803-739&7/02/$17.00 @2002 IEEE

In an adversarial multi-robot task, such as playing robot soccer, decisions for team and single robot behavior must be made quickly to take advantage of short-term fortuitous events when they occur. When no such opportunities exist, the team must execute sequences of coordinated action across team members that increases the likelihood of future opportunities. We have developed a hierarchical architecture, called STP, to control an autonomous team of robots operating in an adversarial environment. STP consists of Skills for executing the low-level actions that make up robot behavior, Tactics for determining what skills to execute, and Plays for coordinating synchronized activity amongst team members. Our STP architecture combines each of these components to achieve autonomous team control. Moreover, the STP hierarchy allows for fast team response in adversarial environments while carrying out actions with longer goals. In this article, we present our STP architecture for controlling an autonomous robot team in a dynamic adversarial task that allows for coordinated team activity towards long-term goals, with the ability to respond rapidly to dynamic events. Secondly, we present the sub-component of skills and tactics as a generalized, single-robot control hierarchy for hierarchical problem decomposition with flexible control policy implementation and reuse. Thirdly, we contribute our play techniques as a generalized method for encoding and synchronizing team behavior, providing multiple competing team responses, and for supporting effective strategy adaptation against opponent teams. STP has been fully implemented on a robot platform and thoroughly tested against a variety of unknown opponent teams under in a number of RoboCup robot soccer competitions. We present these competition results as a mechanism to analyze the performance of STP in a real setting.