Together at Last: Perception and Learning for Collaborative Robots Operating in Human-Centric Environments

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Cobots, or collaborative robots, are a class of machines intended to physically interact with humans in a shared space. This definition encompasses a large variety of hardware systems and application domains (from manufacturing to autonomous vehicles). Over the next five years, this nascent market segment is expected to boom all major robotics manufacturers have established new products in this category. The goal of the proposed research program is to dramatically advance the state of the art in collaborative robotics, making cobots more capable and more safe than at present. This research will bridge the gap and help make cobots ubiquitous outside of laboratories. The program has four key research themes: 1) introspection, sensor fusion, and sensor and actuator self-calibration, 2) semantic mapping, 3) intention recognition for trust-based interaction, and 4) formal testing and validation. These themes are tied together by the unifying thread of advanced perceptual processing, with the aim of creating flexible cobot platforms for the real world.******Research activities across the four themes (or project areas) will build upon results from robotics and machines learning. A general framework for hierarchical perception will be developed, beginning with low-level data fusion and leading to high-level semantic understanding. Data from novel sensing modalities will be fused into consistent environment representations; through self-calibration and introspection, a baseline level of functionality and safety will be maintained at all times. Multimodal sensor data will then be processed to generate semantic maps, which will include information about objects and their relationships (allowing for reasoning about interactions). The maps will be produced using clustering and supervised learning. By extending semantic perception, human intention and activity recognition will become possible. Intention recognition and response learning will significantly enhance safety, ensuring that each party (human and robot) is able to anticipate the actions of the other. Finally, all capabilities will be verified in an extensive test campaign that includes formal validation, using an advanced mobile manipulator.******The proposed research program has the potential for transformative impact in a wide range of industries, fundamentally changing the way people work with robots and, in turn, leveraging robotics to enhance productivity and quality of life. Research outputs will create new technological solutions that will be readily transferable to Canadian companies, giving them the tools they need to enter new economic areas that have tremendous growth potential. Many of these markets, including service robotics and advanced manufacturing (for, e.g., aerospace) will be of critical importance to Canada in maintaining a competitive edge in the global marketplace.