Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers

The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot. How this can be achieved is a challenge that requires addressing both technical as well as human-centric research questions. In this paper we discuss the state of the art in safety assurance, existing as well as emerging standards in this area, and the need for new approaches to safety assurance in the context of learning machines. We then focus on robotic learning from demonstration, the challenges these techniques pose to safety assurance and indicate opportunities to integrate safety considerations into algorithms "by design". Finally, from a human-centric perspective, we stipulate that, to achieve high levels of safety and ultimately trust, the robotic co-worker must meet the innate expectations of the humans it works with. It is our aim to stimulate a discussion focused on the safety aspects of human-in-the-loop robotics, and to foster multidisciplinary collaboration to address the research challenges identified

Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces

To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.Comment: 12 pages, Accepted for publication IEEE Transaction on Robotics 201

The road less travelled: leadership and engagement of learners in a collaborative learning environment

[Abstract]: Historically at USQ we have taught information literacy classes in a very traditional instructional format. The training labs have rows of seating facing forward, and large computer monitors in front of participants. The trainer stands at the front with a whiteboard and projection screen. With innovations in teaching styles and developments in learning spaces, we are this year initiating a change to information literacy classes. Traditional training labs and teaching styles have been replaced by collaborative group learning spaces and a guided teaching style. As part of a refurbishment project, one of our training labs and an office area of the library have been set up to support collaborative learning. Subsequently in Semester 1, 2009 we will begin using the new collaborative learning facilities for all generic information literacy classes, and some faculty classes. In the past our training has focussed on the transmission of information and we have not fully engaged our students in the learning process. We have used PowerPoint’s and the Library website to conduct classes. Whilst the basic information we want to impart to students will be the same, the way we do this and hopefully the learning outcomes will change significantly with the shift to a collaborative teaching style and environment

Summary Assessment Report: The Planning Phase of the Rebuilding Communities Initiative

Evaluates the planning and implementation of a multiyear community change initiative in Boston, Philadelphia, Washington, D.C., Denver, and Detroit