This manuscript primarily aims to enhance the performance of whole-body controllers(WBC) for underactuated legged locomotion. We introduce a systematic parameter design mechanism for the floating-base feedback control within the WBC. The proposed approach involves utilizing the linearized model of unactuated dynamics to formulate a Linear Quadratic Regulator(LQR) and solving a Riccati gain while accounting for potential physical constraints through a second-order approximation of the log-barrier function. And then the user-tuned feedback gain for the floating base task is replaced by a new one constructed from the solved Riccati gain. Extensive simulations conducted in MuJoCo with a point bipedal robot, as well as real-world experiments performed on a quadruped robot, demonstrate the effectiveness of the proposed method. In the different bipedal locomotion tasks, compared with the user-tuned method, the proposed approach is at least 12% better and up to 50% better at linear velocity tracking, and at least 7% better and up to 47% better at angular velocity tracking. In the quadruped experiment, linear velocity tracking is improved by at least 3% and angular velocity tracking is improved by at least 23% using the proposed method.

This article studies capturability and push recovery for quadruped locomotion. Despite the rich literature on capturability analysis and push recovery for legged robots, existing tools have been developed mainly with the requirement of reaching static or quasi-static balance following a push. In practice, this requirement commonly restricts capturability analysis to cases with simple dynamics and fails to encode the time dependence of capturable states for legged locomotion with time-based gaits. To address these issues, we apply switched systems to model quadruped locomotion and extend capturability notions through a novel specification ofdynamic balance. We also provide an explicit model predictive control (EMPC) scheme to compute the dynamic balance and capturable tubes and offer a way of using the capturable tube to synthesize push recovery controllers. Such a generalization allows for a rigorous characterization of disturbance timing on the capturability of quadrupedal locomotion and opens the door of disturbance-timing-aware push recovery control strategies. Extensive simulation and hardware experiments illustrate the necessity of considering dynamic balance for quadrupedal push recovery, reveal how disturbance timing affects capturability, and demonstrate the significant improvement in disturbance rejection with the proposed strategy. Hardware experimental validations on a replica of the Mini Cheetah quadruped further verify that the proposed approach performs statistically better than the state-of-the-art baseline considered.

This paper presents a novel framework for point- foot biped running in three-dimensional space. The proposed approach generates center of mass (CoM) reference trajectories based on a differentially flat spring-loaded inverted pendulum (SLIP) model. A foothold planner is used to select touch down location that renders optimal CoM trajectory for upcoming step in real time. Dynamically feasible trajectories of CoM and orientation are subsequently generated by a simplified single rigid body (SRB) model based model predictive control (MPC). A task-space controller is then applied online to compute whole- body joint torques which embeds these target dynamics into the robot. The proposed approach is evaluated on physical simulation of a 12 degree-of-freedom (DoF), 7.95 kg point-foot bipedal robot. The robot achieves stable running at at varying speeds with maximum value of 1.1 m/s. The proposed scheme is shown to be able to reject vertical disturbances of 8 N. s and lateral disturbance of 6.5 N. s applied at the robot base.

This paper studies autonomous stair climbing for quadrupedal robots with perception. Enabling quadrupeds to reliably climb staircases greatly expands their applicability in practical scenarios. For this structured task, we develop a simple yet effective perception and control framework for autonomous quadrupedal stair climbing. By exploiting the structural knowledge about the staircases, the proposed framework first extracts the geometric information about the staircase from measurements of the perception system. Then, the climbing velocity and associated foothold references during stair climbing are generated via simple optimization algorithms based on the geometric information about the staircase. Given these references, we use model predictive control based approach to generate input joint torques for controlling the quadruped to complete the whole stair climbing task. Simulation validations using the full dynamic model of the Unitree’s Aliengo quadruped with the MuJoCo simulator are performed, which demonstrate successful autonomous climbing of various staircases with different geometries. Effectiveness of the proposed strategy is further validated through hardware experiments on the real Aliengo robot with different real-world staircases.