Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge

Hubungan di antara pengaturan kerja fleksibel dan prestasi pekerja dalam kalangan ejen insurans wanita

Ejen insurans merupakan jurujual pertengahan bagi syarikat insurans di mana mereka memainkan peranan penting dalam memberi khidmat nasihat kewangan (Hannah, 2011). Ejen insurans bekerja berdasarkan persekitaran pengaturan kerja yang fleksibel di mana mereka boleh menyediakan jadual waktu bekerja sendiri. Sebahagian daripada mereka bertemu dengan pelanggan pada waktu perniagaan siang hari, sementara yang lain pula membuat kertas kerja dan menyediakan konsultasi untuk pelanggan pada waktu petang. Kebanyakan mereka bekerja selama 40 jam seminggu dan ada juga beberapa ejen yang bekerja lebih lama daripada 40 jam (Hannah, 2011). Prestasi ejen insurans sangat penting untuk mengekalkan jenama produk insurans. Penilaian terhadap prestasi di kalangan ejen insurans biasanya bergantung kepada kejayaan atau kegagalan mencapai sasaran penjualan (Insurance Agent Job Overview, 2019). Proses menjual produk insurans memerlukan masa kerana mereka perlu mendekati pelanggan sebanyak mungkin dan ketersediaan waktu bekerja yang tidak tetap

Technology for an intelligent, free-flying robot for crew and equipment retrieval in space

Crew rescue and equipment retrieval is a Space Station Freedom requirement. During Freedom's lifetime, there is a high probability that a number of objects will accidently become separated. Members of the crew, replacement units, and key tools are examples. Retrieval of these objects within a short time is essential. Systems engineering studies were conducted to identify system requirements and candidate approaches. One such approach, based on a voice-supervised, intelligent, free-flying robot was selected for further analysis. A ground-based technology demonstration, now in its second phase, was designed to provide an integrated robotic hardware and software testbed supporting design of a space-borne system. The ground system, known as the EVA Retriever, is examining the problem of autonomously planning and executing a target rendezvous, grapple, and return to base while avoiding stationary and moving obstacles. The current prototype is an anthropomorphic manipulator unit with dexterous arms and hands attached to a robot body and latched in a manned maneuvering unit. A precision air-bearing floor is used to simulate space. Sensor data include two vision systems and force/proximity/tactile sensors on the hands and arms. Planning for a shuttle file experiment is underway. A set of scenarios and strawman requirements were defined to support conceptual development. Initial design activities are expected to begin in late 1989 with the flight occurring in 1994. The flight hardware and software will be based on lessons learned from both the ground prototype and computer simulations

Towards safe human-to-robot handovers of unknown containers

Safe human-to-robot handovers of unknown objects require accurate estimation of hand poses and object properties, such as shape, trajectory, and weight. Accurately estimating these properties requires the use of scanned 3D object models or expensive equipment, such as motion capture systems and markers, or both. However, testing handover algorithms with robots may be dangerous for the human and, when the object is an open container with liquids, for the robot. In this paper, we propose a real-to-simulation framework to develop safe human-to-robot handovers with estimations of the physical properties of unknown cups or drinking glasses and estimations of the human hands from videos of a human manipulating the container. We complete the handover in simulation, and we estimate a region that is not occluded by the hand of the human holding the container. We also quantify the safeness of the human and object in simulation. We validate the framework using public recordings of containers manipulated before a handover and show the safeness of the handover when using noisy estimates from a range of perceptual algorithms