2020. 3rd IEEE International Conference on Soft Robotics (RoboSoft), April 6-9, 2020. Authors: Wei-Hsi Chen, Shivangi Misra, J. Diego Caporale, Daniel E. Koditschek, Shu Yang, Cynthia R. Sung. Our prior work demonstrated that a REBO structure could be used to juggle (repeatedly loft and catch) a 1kg load. Here, we modify that design to achieve actual locomotion: translation of the mechanism’s mass center through a two degree-of-freedom workspace.

2020. IEEE Robotics and Automation Letter. Authors: Wei-Hsi Chen, Shivangi Misra, Yuchong Gao, Young-Joo Lee, Daniel E. Koditschek, Shu Yang, Cynthia R. Sung. We present an approach to overcoming challenges in dynamical dexterity for robots through programmably compliant origami mechanisms.

2019. The International Symposium on Robotics Research (ISRR 2019) Authors: T. Turner Topping, Vasileios Vasilopoulos, Avik De and Daniel E. Koditschek We document the reliably repeatable dynamical mounting and dismounting of wheeled stools and carts, and of fixed ledges, by the Minitaur robot. Because these tasks span a range of length scales that preclude quasi-static execution, we use a hybrid dynamical systems framework to variously compose and thereby systematically reuse a small lexicon of templates (low degree of freedom behavioral primitives). The resulting behaviors comprise the key competences beyond mere locomotion required for robust implementation on a legged mobile manipulator of a simple version of the warehouseman’s problem.

2019. Authors: Garrett Wenger, Avik De, Daniel Koditschek. The Jerboa, a tailed bipedal robot with two hip-actuated, passive-compliant legs and a doubly actuated tail, has been shown both formally and empirically to exhibit a variety of stable hopping and running gaits in the sagittal plane. In this paper we take the first steps toward operating Jerboa as a fully spatial machine by addressing the predominant mode of destabilization away from the sagittal plane: body roll. We develop a provably stable controller for underactuated aerial stabilization of the coupled body roll and tail angles, that uses just the tail torques. We show that this controller is successful at reliably reorienting the Jerboa body in roughly 150 ms of freefall from a large set of initial conditions. This controller also enables (and appears intuitively to be crucial for) sustained empirically stable hopping in the frontal plane by virtue of its substantial robustness against destabilizing perturbations and calibration errors. The controller as well as the analysis methods developed here are applicable to any robotic platform with a similar doubly-actuated spherical tail joint.

2018. Authors: Omur Arslan and Daniel E. Koditschek. We construct a sensor-based feedback law that provably solves the real-time collision-free robot navigation problem in a compact convex Euclidean subset cluttered with unknown but sufficiently separated and strongly convex obstacles. Our algorithm introduces a novel use of separating hyperplanes for identifying the robot’s local obstacle-free convex neighborhood, affording a reactive (online-computed) continuous and piecewise smooth closed-loop vector field whose smooth flow brings almost all configurations in the robot’s free space to a designated goal location, with the guarantee of no collisions along the way. Specialized attention to planar navigable environments yields a necessary and sufficient condition on convex obstacles for almost global navigation towards any goal location in the environment. We further extend these provable properties of the planar setting to practically motivated limited range, isotropic and anisotropic sensing models, and the nonh! olonomically constrained kinematics of the standard differential drive vehicle. We conclude with numerical and experimental evidence demonstrating the effectiveness of the proposed sensory feedback motion planner.

2018. We demonstrate the physical rearrangement of wheeled stools in a moderately cluttered indoor environment by a quadrupedal robot that autonomously achieves a user’s desired configuration. The robot’s behaviors are planned and executed by a three layer hierarchical architecture consisting of: an offline symbolic task and motion planner; a reactive layer that tracks the reference output of the deliberative layer and avoids unanticipated obstacles sensed online; and a gait layer that realizes the abstract unicycle commands from the reactive module through appropriately coordinated joint level torque feedback loops. This work also extends prior formal results about the reactive layer to a broad class of nonconvex obstacles. Our design is verified both by formal proofs as well as empirical demonstration of various assembly tasks.

2018. This paper applies an extension of classical averaging methods to hybrid dynamical systems, thereby achieving formally specified, physically effective and robust instances of all virtual bipedal gaits on a quadrupedal robot. Gait specification takes the form of a three parameter family of coupling rules mathematically shown to stabilize limit cycles in a low degree of freedom template: an abstracted pair of vertical hoppers whose relative phase locking encodes the desired physical leg patterns. These coupling rules produce the desired gaits when appropriately applied to the physical robot.

This paper demonstrates a fully sensor-based reactive homing behavior on a physical quadrupedal robot, using onboard sensors, in simple (convex obstacle-cluttered) unknown, GPS-denied environments. Its implementation is enabled by our empirical success in controlling the legged machine to approximate the (abstract) unicycle mechanics assumed by the navigation algorithm, and our proposed method of range-only target localization using particle filters.

2017. We document empirically stable bounding using an actively powered spine on the Inu quadrupedal robot, and propose a reduced-order model to capture the dynamics associated with this additional, actuated spine degree of freedom. This model is sufficiently accurate as to roughly describe the robots mass center trajectory during a bounding limit cycle, thus making it a potential option for low dimensional representations of spine actuation in steady-state legged locomotion.

This video documents our field experiments at White Sands National Monument with the RHex robot, in March 2016. It demonstrates the great potential for RHex to assist aeolian scientists in desert research. By collecting data through sensors mounted on RHex, we gather transformative datasets that are required to calibrate and verify existing and future dune dynamics and sand transport models. This work is produced by Nicholas Lancaster, Desert Research Institute, and will be presented at the 2016 Geological Society of America Conference.

The Penn Jerboa showcases new leaping behaviors and demonstrates an innovative method of describing and categorizing these leaps across robot platforms. Ghost Minitaur™ is a patent-pending medium-sized legged robot highly adept at perceiving tactile sensations. Its high torque motors, motor controllers, and specialized leg design allow this machine run and jump over difficult terrain, climb fences and stairs, and even open doors. High-speed and high-resolution encoders let the robot see and feel the ground through the motors and adapt faster than the blink of an eye.  Minitaur was developed in Kod*lab.

Playlist of all of Kod*lab Research Videos