PAKE: Learning Whole-Body Loco-Manipulation with Partial Kinematic Embeddings

2026-07-12

AI Summary

This work addresses the challenges of high-dimensional action spaces, underutilized redundancy, and insufficient control accuracy in high-precision whole-body loco-manipulation for highly articulated robots. The authors propose a hierarchical control framework wherein a high-level planner leverages Kinematic Normalizing Flows to generate diverse, kinematically feasible partial reference trajectories in a latent space, effectively exploring redundant solutions. A low-level controller then employs imitation learning to accurately track these references while ensuring physical feasibility. By integrating a large-scale kinematic dataset with high-dimensional action modeling, the approach significantly outperforms existing methods in simulation. Hardware experiments across eight tasks and 24 trials demonstrate state-of-the-art performance, achieving end-effector pose errors of 4.5 cm and 0.14 rad, as well as mobile tracking errors of 0.1 m/s and 0.01 rad/s.

Abstract

Loco-manipulation has recently shown promising capabilities; however, achieving high-precision control, managing the high-dimensional action space induced by many degrees of freedom (DoFs), and fully exploiting the inherent redundancy of whole-body systems remain challenging. In this paper, we propose a novel whole-body control framework that effectively addresses these challenges by decomposing the complex loco-manipulation problem into partial reference motion generation and low-level imitation control. We introduce a new Kinematic Normalizing Flow (KNF) model, trained on a large-scale kinematic dataset, that generates diverse yet feasible partial reference motions. A high-level controller is then trained to navigate the KNF's latent space to exploit redundant solutions, while a low-level controller ensures physically feasible and accurate motion execution. We validate our approach on the quadrupedal robot equipped with a six-DoF robotic arm. In simulation, experimental results show that our approach significantly outperforms state-of-the-art methods in terms of tracking accuracy and feasible workspace coverage. For hardware deployment, we evaluate the system over 24 episodes across 8 different mobile loco-manipulation tasks. The system achieves end-effector pose-tracking errors of 4.5 cm and 0.14 rad, while maintaining accurate locomotion tracking with linear and angular velocity errors of 0.1 m/s and 0.01 rad/s, respectively, outperforming competitive baselines. Our method represents a practical and powerful solution for accurate and generalized whole-body loco-manipulation in high-DoF robotic systems, with promising potential for diverse downstream robotic tasks.

Problem

• loco-manipulation
• whole-body control
• high-dimensional action space
• kinematic redundancy
• high-DoF robotic systems

Innovation

• Kinematic Normalizing Flow
• Whole-body Loco-Manipulation
• Partial Kinematic Embeddings
• High-DoF Robotic Control
• Latent Space Navigation