Adapting Gait Frequency for Posture-regulating Humanoid Push-recovery via Hierarchical Model Predictive Control

Current humanoid push-recovery strategies often use whole-body motion, yet posture regulation is often overlooked. For instance, during manipulation tasks, the upper body may need to stay upright and have minimal recovery displacement. This paper introduces a novel approach to enhancing humanoid push-recovery performance under unknown disturbances and regulating body posture by tailoring the recovery stepping strategy. We propose a hierarchical-MPC-based scheme that analyzes and detects instability in the prediction window and quickly recovers through adapting gait frequency. Our approach integrates a high-level nonlinear MPC, a posture-aware gait frequency adaptation planner, and a low-level convex locomotion MPC. The planners predict the center of mass (CoM) state trajectories that can be assessed for precursors of potential instability and posture deviation. In simulation, we demonstrate improved maximum recoverable impulse by 131% on average compared with baseline approaches. In hardware experiments, a 125 ms advancement in recovery stepping timing/reflex has been observed with the proposed approach, We also demonstrate improved push-recovery performance and minimized attitude change under 0.2 rad.

Dynamic Loco-manipulation on HECTOR: Humanoid for Enhanced ConTrol and Open-source Research

Despite their remarkable advancement in locomotion and manipulation, humanoid robots remain challenged by a lack of synchronized loco-manipulation control, hindering their full dynamic potential. In this work, we introduce a versatile and effective approach to controlling and generalizing dynamic locomotion and loco-manipulation on humanoid robots via a Force-and-moment-based Model Predictive Control (MPC). Specifically, we proposed a simplified rigid body dynamics (SRBD) model to take into account both humanoid and object dynamics for humanoid loco-manipulation. This linear dynamics model allows us to directly solve for ground reaction forces and moments via an MPC problem to achieve highly dynamic real-time control. Our proposed framework is highly versatile and generalizable. We introduce HECTOR (Humanoid for Enhanced ConTrol and Open-source Research) platform to demonstrate its effectiveness in hardware experiments. With the proposed framework, HECTOR can maintain exceptional balance during double-leg stance mode, even when subjected to external force disturbances to the body or foot location. In addition, it can execute 3-D dynamic walking on a variety of uneven terrains, including wet grassy surfaces, slopes, randomly placed wood slats, and stacked wood slats up to 6 cm high with the speed of 0.6 m/s. In addition, we have demonstrated dynamic humanoid loco-manipulation over uneven terrain, carrying 2.5 kg load. HECTOR simulations, along with the proposed control framework, are made available as an open-source project. (https://github.com/DRCL-USC/Hector_Simulation).

Balancing Control and Pose Optimization for Wheel-legged Robots Navigating Uneven Terrains

In this paper, we propose a novel approach on controlling wheel-legged quadrupedal robots using pose optimization and force control via quadratic programming (QP). Our method allows the robot to leverage wheel torques to navigate the terrain while keeping the wheel traction and balancing the robot body. In detail, we present a rigid body dynamics with wheels that can be used for real-time balancing control of wheel-legged robots. In addition, we introduce an effective pose optimization method for wheel-legged robot's locomotion over uneven terrains with ramps and stairs. The pose optimization utilized a nonlinear programming (NLP) solver to solve for the optimal poses in terms of joint positions based on kinematic and contact constraints during a stair-climbing task with rolling wheels. In simulation, our approach has successfully validated for the problem of a wheel-legged robot climbing up a 0.34m stair with a slope angle of 80 degrees and shown its versatility in multiple-stair climbing with varied stair runs and rises with wheel traction. Experimental validation on the real robot demonstrated the capability of climbing up on a 0.25m stair with a slope angle of 30 degrees.