Beyond Human-Derived Motion: Exploring Robot-Specific Execution for Construction Humanoids

Key points

• Construction productivity has remained a per- sistent challenge, motivating growing interest in humanoid robots that can operate in workspaces and with tools designed for human workers.1
• Realizing this potential requires effective motion control, for which human-derived motion transfer has emerged as a promising approach by capturing and retargeting human demonstrations to humanoid robots.1
• On an adult-sized Unitree H1-2, Human- Derived Motion was generated through vision-based capture and retargeting, whereas Robot-Specific Motion used inverse kinematics and minimum-jerk transfers to reach the same targets while departing from the demonstrated posture, as verified using Rapid Upper Limb Assessment (RULA).1
• Across a pick-and-stack sequence representing construction material handling, with four matched motion targets and three trials per condition, Robot-Specific Motion achieved 12.87 s versus 25.03 s in execution time, 0.92 m versus 4.33 m in trajectory length, and 58.7 J versus 247.3 J in two-arm mechanical work.1

Sources (1)

• [1]Beyond Human-Derived Motion: Exploring Robot-Specific Execution for Construction Humanoids
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 11:20 PM

  This study examines whether such Robot-Specific Motion offers an advantage in motion execution efficiency over Human- Derived Motion.

  Construction productivity has remained a per- sistent challenge, motivating growing interest in humanoid robots that can operate in workspaces and with tools designed for human workers.

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