A real-world prehensile pushing example. The trajectory the robot followed is based on our method. As indicated by the arrows, the robot only translates in the three leftmost images, while it translates and rotates in the three rightmost images.
A real-world prehensile pushing example. The trajectory the robot followed is based on our method. As indicated by the arrows, the robot only translates in the three leftmost images, while it translates and rotates in the three rightmost images.
We address prehensile pushing, the problem of manipulating a grasped object by pushing against the environment. Our solution is an efficient nonlinear trajectory optimization problem relaxed from an exact mixed integer non-linear trajectory optimization formulation. The critical insight is recasting the external pushers (environment) as a discrete probability distribution instead of binary variables and minimizing the entropy of the distribution. The probabilistic reformulation allows all pushers to be used simultaneously, but at the optimum, the probability mass concentrates onto one due to the entropy minimization. We numerically compare our method against a state-of-the-art sampling-based baseline on a prehensile pushing task. The results demonstrate that our method finds trajectories 8 times faster and at a 20 times lower cost than the baseline. Finally, we demonstrate that a simulated and real Franka Panda robot can successfully manipulate different objects following the trajectories proposed by our method.
This work was primarily supported by ERC AdV grant BIRD (884807), Knut and Alice Wallenberg Foundation, and the Swedish Research Council. The work was also partially supported by Flanders Make, the research center of manufacturing industry in Belgium https://www.flandersmake.be/en.