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1. Planning for the Unexpected: Explicitly Optimizing Motions for Ground Uncertainty in Running

   Green, Kevin ; Hatton, Ross ; Hurst, Jonathan ( January 2020 , IEEE International Conference on Robotics and Automation)

   null (Ed.)

   We propose a method to generate actuation plans for a reduced order, dynamic model of bipedal running. This method explicitly enforces robustness to ground uncertainty. The plan generated is not a fixed body trajectory that is aggressively stabilized: instead, the plan interacts with the passive dynamics of the reduced order model to create emergent robustness. The goal is to create plans for legged robots that will be robust to imperfect perception of the environment, and to work with dynamics that are too complex to optimize in real-time. Working within this dynamic model of legged locomotion, we optimize a set of disturbance cases together with the nominal case, all with linked inputs. The input linking is nontrivial due to the hybrid dynamics of the running model but our solution is effective and has analytical gradients. The optimization procedure proposed is significantly slower than a standard trajectory optimization, but results in robust gaits that reject disturbances extremely effectively without any replanning required. 

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2. Contact-Implicit Optimization of Locomotion Trajectories for a Quadrupedal Microrobot

   https://doi.org/10.15607/RSS.2018.XIV.041  

   Doshi, Neel ; Jayaram, Kaushik ; Goldberg, Benjamin ; Manchester, Zachary ; Wood, Robert ; Kuindersma, Scott ( June 2018 , Robotics: Science and Systems)

   Planning locomotion trajectories for legged microrobots is challenging. This is because of their complex morphology, high frequency passive dynamics, and discontinuous contact interactions with their environment. Consequently, such research is often driven by time-consuming experimental methods. As an alternative, we present a framework for systematically modeling, planning, and controlling legged microrobots. We develop a three- dimensional dynamic model of a 1.5 g quadrupedal microrobot with complexity (e.g., number of degrees of freedom) similar to larger-scale legged robots. We then adapt a recently developed variational contact-implicit trajectory optimization method to generate feasible whole-body locomotion plans for this microrobot, and demonstrate that these plans can be tracked with simple joint-space controllers. We plan and execute periodic gaits at multiple stride frequencies and on various surfaces. These gaits achieve high per-cycle velocities, including a maximum of 10.87 mm/cycle, which is 15% faster than previously measured for this microrobot. Furthermore, we plan and execute a vertical jump of 9.96 mm, which is 78% of the microrobot’s center-of- mass height. To the best of our knowledge, this is the first end-to-end demonstration of planning and tracking whole-body dynamic locomotion on a millimeter-scale legged microrobot. 

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3. An Articulated Closed Kinematic Chain Planar Robotic Leg for High-Speed Locomotion

   https://doi.org/10.1115/1.4045689  

   Liu, Yujiong ; Ben-Tzvi, Pinhas ( August 2020 , Journal of Mechanisms and Robotics)

   Abstract This paper presents the design, dynamic modeling, and integration of a single degree of freedom (DOF) robotic leg mechanism intended for tailed quadruped locomotion. The design employs a lightweight six-bar linkage that couples the hip and knee flexion/extension joints mechanically, requiring only a single degree of actuation. By utilizing a parametric optimization, a unique topological arrangement is achieved that results in a foot trajectory that is well suited for dynamic gaits including trot-running, bounding, and galloping. Furthermore, a singular perturbation is introduced to the hybrid dynamic framework to address the lack of robust methods that provide a solution for the differential algebraic equations (DAEs) that characterize closed kinematic chain (CKC) structures as well as the hybrid nature of legged locomotion. By approximating the system dynamics as ordinary differential equations (ODEs) and asymptotically driving the constraint error to zero, CKCs can adopt existing real-time model-based/model-predictive/hybrid-control frameworks. The dynamic model is verified through simulations and the foot trajectory was experimentally validated. Preliminary open-loop planar running demonstrated speeds up to 3.2 m/s. These advantages, accompanied by low-integration costs, warrant this leg as a robust, effective platform for future tailed quadruped research. 

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4. Optimizing Bipedal Maneuvers of Single Rigid-Body Models for Reinforcement Learning

   https://doi.org/10.1109/Humanoids53995.2022.9999741  

   Batke, Ryan ; Yu, Fangzhou ; Dao, Jeremy ; Hurst, Jonathan ; Hatton, Ross L. ; Fern, Alan ; Green, Kevin ( November 2022 , 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids))

   In this work, we propose a method to generate reduced-order model reference trajectories for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real reinforcement learning. Our approach is to utilize a single rigid-body model (SRBM) to optimize libraries of trajectories offline to be used as expert references that guide learning by regularizing behaviors when incorporated in the reward function of a learned policy. This method translates the model's dynamically rich rotational and translational behavior to a full-order robot model and successfully transfers to real hardware. The SRBM's simplicity allows for fast iteration and refinement of behaviors, while the robustness of learning-based controllers allows for highly dynamic motions to be transferred to hardware. Within this work we introduce a set of transferability constraints that amend the SRBM dynamics to actual bipedal robot hardware, our framework for creating optimal trajectories for a variety of highly dynamic maneuvers as well as our approach to integrating reference trajectories for a high-speed running reinforcement learning policy. We validate our methods on the bipedal robot Cassie on which we were successfully able to demonstrate highly dynamic grounded running gaits up to 3.0 m/s. 

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5. Motion Planning for Agile Legged Locomotion using Failure Margin Constraints

   https://doi.org/10.1109/IROS47612.2022.9981903  

   Green, Kevin ; Warila, John ; Hatton, Ross L. ; Hurst, Jonathan ( October 2022 , 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   The complex dynamics of agile robotic legged locomotion requires motion planning to intelligently adjust footstep locations. Often, bipedal footstep and motion planning use mathematically simple models such as the linear inverted pendulum, instead of dynamically-rich models that do not have closed-form solutions. We propose a real-time optimization method to plan for dynamical models that do not have closed form solutions and experience irrecoverable failure. Our method uses a data-driven approximation of the step-to-step dynamics and of a failure margin function. This failure margin function is an oriented distance function in state-action space where it describes the signed distance to success or failure. The motion planning problem is formed as a nonlinear program with constraints that enforce the approximated forward dynamics and the validity of state-action pairs. For illustration, this method is applied to create a planner for an actuated spring-loaded inverted pendulum model. In an ablation study, the failure margin constraints decreased the number of invalid solutions by between 24 and 47 percentage points across different objectives and horizon lengths. While we demonstrate the method on a canonical model of locomotion, we also discuss how this can be applied to data-driven models and full-order robot models. 

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