Exploring Kinodynamic Fabrics for Reactive Whole-Body Control of Underactuated Humanoid Robots

For bipedal humanoid robots to successfully operate in the real world, they must be competent at simultaneously executing multiple motion tasks while reacting to unforeseen external disturbances in real-time. We propose Kinodynamic Fabrics as an approach for the specification, solution and simultaneous execution of multiple motion tasks in real-time while being reactive to dynamism in the environment. Kinodynamic Fabrics allows for the specification of prioritized motion tasks as forced spectral semi-sprays and solves for desired robot joint accelerations at real-time frequencies. We evaluate the capabilities of Kinodynamic fabrics on diverse physically challenging whole-body control tasks with a bipedal humanoid robot both in simulation and in the real-world. Kinodynamic Fabrics outperforms the state-of-the-art Quadratic Program based whole-body controller on a variety of whole-body control tasks on run-time and reactivity metrics in our experiments. Our open-source implementation of Kinodynamic Fabrics as well as robot demonstration videos can be found at this url: https://adubredu.github.io/kinofabs

Long-Horizon Planning Under Uncertainty and Geometric Constraints for Mobile Manipulation by Autonomous Humanoid Robots

Autonomous humanoid robots have the potential to perform critical and labor-intensive tasks that could go a long way to improve upon the quality of human life. To realize this potential, an autonomous humanoid robot must be capable of planning the right set of long-horizon actions under the conditions of uncertainty and geometric constraints that characterize real-world environments. This thesis proposes long-horizon planning approaches for humanoid robots under conditions of uncertainty and geometric constraints that are typical of real-world environments. The specific contributions of this thesis are, 1) A reactive and efficient task planning approach for planning under low-entropy conditions in the robot's belief of the state of the world, 2) A reactive and probabilistic long-horizon planning approach for long-horizon tasks under state estimation and action uncertainty and 3) An optimal long-horizon planning approach for geometrically constrained tasks based on mixed integer convex programming. We demonstrate the effectiveness of the approaches presented in this thesis on object rearrangement and mobile manipulation tasks in a domestic environment using the Agility Robotics Digit Bipedal Humanoid Robot and evaluate the presented approaches on planning time and task success rate metrics.PhDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/178010/1/adubredu_1.pd