Humanoid Loco-Manipulation

Executing reliable Humanoid-Object Interaction (HOI) tasks for humanoid robots is hindered by the lack of generalized control interfaces and robust closed-loop perception mechanisms. In this work, we introduce Perceptive Root-guided Humanoid-Object Interaction, Pro-HOI, a generalizable framework for robust humanoid loco-manipulation. First, we collect box-carrying motions that are suitable for real-world deployment and optimize penetration artifacts through a Signed Distance Field loss. Second, we propose a novel training framework that conditions the policy on a desired root-trajectory while utilizing reference motion exclusively as a reward. This design not only eliminates the need for intricate reward tuning but also establishes root trajectory as a universal interface for high-level planners, enabling simultaneous navigation and loco-manipulation. Furthermore, to ensure operational reliability, we incorporate a persistent object estimation module. By fusing real-time detection with Digital Twin, this module allows the robot to autonomously detect slippage and trigger re-grasping maneuvers. Empirical validation on a Unitree G1 robot demonstrates that Pro-HOI significantly outperforms baselines in generalization and robustness, achieving reliable long-horizon execution in complex real-world scenarios.

Visual loco-manipulation of arbitrary in-the-wild objects requires accurate end-effector (EE) control and a generalizable understanding of the scene from visual inputs (eg, RGB-D images). Existing imitation and sim2real methods jointly learn both these aspects via monolithic end-to-end learning and are thus hard to scale. In this work, we bring to bear the best tools for each of these problems -- large vision models for generalizable scene understanding and simulated training for accurate EE control -- leading to an overall modular loco-manipulation system that exhibits strong generalization. Our core technical innovation is HERO, an accurate residual-aware EE tracking policy made possible by combining classical robotics with machine learning. It uses a) inverse kinematics to convert residual end-effector targets into reference trajectories, b) a learned neural forward model for accurate forward kinematics, and c) goal adjustment and replanning. Together, these innovations reduce the end-effector tracking error to 2.44cm, outperforming the strongest prior method by 5.5x. Our overall system operates in diverse real-world environments, from offices to coffee shops, where the robot reliably grasps various everyday objects (eg, mugs, apples, toys) on surfaces ranging from 43cm to 92cm in height. Systematic modular and end-to-end tests demonstrate the effectiveness of our proposed design. We believe our advances open up new ways of training humanoids to interact with daily objects. Project page: https://hero-humanoid.github.io/

Maintaining whole-body stability and motion tracking under large interaction forces remains challenging for humanoids. We present Thor, a reinforcement learning framework for forceful humanoid loco-manipulation. Thor jointly trains lower-body, waist, and upper-body policies with shared whole-body observations and body-specific rewards to coordinate locomotion and force adaptation, waist posture regulation, and upper-body motion tracking. We further introduce a force-adaptive torso-tilt (FAT2) objective that derives a load-dependent horizontal center-of-mass offset reference from quasi-static moment balance. Capacity-matched simulation blations show that the three-policy architecture improves tracking under large external force disturbances, while real-world ablations demonstrate that FAT2 increases peak pulling capability. On the Unitree G1, Thor achieves mean peak dual-hand pulling forces of 167.7 N and 145.5 N during backward and forward locomotion, exceeding the best-performing baseline by 68.9% and 74.7%, respectively. Real-world demonstrations include opening a fire door with one hand using approximately 60 N of pulling force and towing a 1.7-ton passenger car.