Stability-Aware Retargeting for Humanoid Multi-Contact Teleoperation

2025-10-05

AI Summary

In teleoperated humanoid locomotion involving multiple non-coplanar contacts, motor torque saturation and contact sliding instability arise due to hand–surface interactions. To address this, we propose a centroidal-dynamics–based stability reorientation method. Our approach formulates and analytically derives the gradient of stability margin—enabling real-time identification and local optimization of stability-sensitive teleoperation setpoints. By dynamically adjusting contact locations and whole-body posture, it enhances motion robustness under complex multi-contact conditions. The method integrates centroidal dynamics modeling, stability margin gradient analysis, and an online reorientation algorithm, validated in both simulation and on physical hardware (e.g., Unitree H1). Experiments demonstrate significant improvements in stability margin, enhanced resilience to external disturbances, and increased joint torque headroom.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Teleoperation is a powerful method to generate reference motions and enable humanoid robots to perform a broad range of tasks. However, teleoperation becomes challenging when using hand contacts and non-coplanar surfaces, often leading to motor torque saturation or loss of stability through slipping. We propose a centroidal stability-based retargeting method that dynamically adjusts contact points and posture during teleoperation to enhance stability in these difficult scenarios. Central to our approach is an efficient analytical calculation of the stability margin gradient. This gradient is used to identify scenarios for which stability is highly sensitive to teleoperation setpoints and inform the local adjustment of these setpoints. We validate the framework in simulation and hardware by teleoperating manipulation tasks on a humanoid, demonstrating increased stability margins. We also demonstrate empirically that higher stability margins correlate with improved impulse resilience and joint torque margin.

Problem

• Preventing motor torque saturation during humanoid teleoperation with contacts
• Enhancing stability against slipping on non-coplanar contact surfaces
• Improving impulse resilience and joint torque margins through stability optimization

Innovation

• Centroidal stability-based retargeting method dynamically adjusts contacts
• Efficient analytical calculation of stability margin gradient
• Local adjustment of teleoperation setpoints enhances stability margins