5 accepted papers
Robot motion planning via probabilistic inference renders a unique viewpoint on the trajectory optimization problem, in which the joint distribution of motion objectives is represented as a factor graph. Thus, the objectives are solved by obtaining the Maximum a Posteriori of the factor graph. While
Grasping fragile objects in the presence of un-certainty is a crucial task for robots, that becomes inherently challenging if the manipulator in use is an industrial robot platform that does not provide compliant control inputs. This requires not only to estimate the alignment error during object co…
Gaussian Process (GP) formulation of continuous-time trajectory offers a fast solution to the motion planning problem via probabilistic inference on factor graph. However, often the solution converges to in-feasible local minima and the planned trajectory is not collision-free. We propose a message…
With the integration of Human-Robot Collaboration (HRC) in industrial assembly scenarios, robot systems face numerous challenges. In contrast to classic robot systems which follow a pre-programmed and fixed sequence of actions, an interaction scenario with humans in the loop requires mutual adaptati…
With the increasing demand of humans and robots collaborating in a confined workspace, it is essential that robots can react instantaneously to unforeseen events while respecting comfort of human co-workers. As the human pays especial attention to the end-effector movement rather than individual joi…