HL-IK: A Lightweight Implementation of Human-Like Inverse Kinematics in Humanoid Arms

2025-09-24

AI Summary

Traditional inverse kinematics (IK) for redundant humanoid arms overly prioritizes end-effector tracking, resulting in non-humanlike joint configurations. To address this, we propose a lightweight anthropomorphic IK framework. Our method introduces FiLM-modulated Spatio-Temporal Attention (FiSTA) networks, trained on large-scale human motion data to learn elbow motion priors, and integrates them as residual corrections into a Levenberg–Marquardt optimizer—enabling plug-and-play anthropomorphism using only end-effector targets and past motion sequences, without requiring real-time full-body perception. Experiments across 183,000 simulated steps show 30.6% and 35.4% reductions in human similarity error for arm position and orientation, respectively. Hardware teleoperation further validates cross-platform effectiveness and substantial improvement in anthropomorphic performance.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Traditional IK methods for redundant humanoid manipulators emphasize end-effector (EE) tracking, frequently producing configurations that are valid mechanically but not human-like. We present Human-Like Inverse Kinematics (HL-IK), a lightweight IK framework that preserves EE tracking while shaping whole-arm configurations to appear human-like, without full-body sensing at runtime. The key idea is a learned elbow prior: using large-scale human motion data retargeted to the robot, we train a FiLM-modulated spatio-temporal attention network (FiSTA) to predict the next-step elbow pose from the EE target and a short history of EE-elbow states.This prediction is incorporated as a small residual alongside EE and smoothness terms in a standard Levenberg-Marquardt optimizer, making HL-IK a drop-in addition to numerical IK stacks. Over 183k simulation steps, HL-IK reduces arm-similarity position and direction error by 30.6% and 35.4% on average, and by 42.2% and 47.4% on the most challenging trajectories. Hardware teleoperation on a robot distinct from simulation further confirms the gains in anthropomorphism. HL-IK is simple to integrate, adaptable across platforms via our pipeline, and adds minimal computation, enabling human-like motions for humanoid robots. Project page: https://hl-ik.github.io/

Problem

• Traditional IK methods produce mechanically valid but unnatural arm motions
• Achieving human-like arm configurations without full-body sensing at runtime
• Integrating human-like motion prediction into standard numerical IK optimization

Innovation

• Learned elbow prior from human motion data
• FiSTA network predicts next-step elbow pose
• Residual term in Levenberg-Marquardt optimizer