ForceBench: Benchmarking Force Integration for Robotic Manipulation

Key points

• Force feedback provides vision-language-action (VLA) policies with physical observations that can guide action selection and execution.1
• However, differences in tasks, demonstrations, and control settings across existing systems hinder reliable comparisons of force integration designs.1
• ForceBench combines eight tasks spanning physical-property inference, resistance-dependent interaction, and contact-rich assembly with 1,600 synchronized demonstrations and task-specific success criteria.1
• We compare one no-force baseline and six force integration designs on a shared $π{0.5}$ backbone.1

Sources (1)

• [1]ForceBench: Benchmarking Force Integration for Robotic Manipulation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 05:28 AM

  We introduce ForceBench, a simulation benchmark for evaluating six-axis wrist-force integration under a unified protocol.

  Force feedback provides vision-language-action (VLA) policies with physical observations that can guide action selection and execution.

Extractive summary: sentences quoted from the sources.