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

TL;DR

This work addresses the challenge of achieving synchronized loco-manipulation in humanoids by introducing a Force-and-moment-based Model Predictive Control (MPC) framework that operates on a Simplified Rigid Body Dynamics (SRBD) model. The key idea is to incorporate both humanoid and object dynamics through an external force model, enabling dynamic 3-D locomotion and loco-manipulation without reliance on high-frequency whole-body control. The authors present HECTOR, a compact, torque-controlled humanoid with a novel hybrid transmission, and demonstrate stability, multi-terrain walking up to 0.6 m/s, and dynamic loco-manipulation with a 2.5 kg payload, validated in hardware and open-sourced for research. The approach offers a practical, computationally efficient baseline for dynamic humanoid control that can support future integration with reinforcement learning and autonomous object handling, thereby accelerating accessible humanoid robotics research.

Abstract

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).

Figures (13)

• Figure 1: HECTOR experiment results: (a) Dynamically walking over random and unstable wood slats with 2.5 kg payload; (b) Balancing in-place while carrying 8 kg payload (50$\%$ robot mass); (c) Stepping in-place while carrying 4 kg payload; (d) Balancing when constantly moving the seesaw; (e) Dynamic turning with 2.5 kg payload (f) Walking up and down 18$^\circ$ slope; (g) Walking on grass with random wood slats. Full result video: https://youtu.be/-r0QoxQgshk
• Figure 2: HECTOR leg range of motion is illustrated by the range of motion of hip roll, thigh, and knee joints.
• Figure 3: HECTOR's arm and leg kinematic definitions and exploded view of transmission systems.
• Figure 4: System and Control Architecture of HECTOR.
• Figure 5: Development of Humanoid SRBD in Locomotion and Loco-manipulation.