Cross-Embodiment Affordance Transfer for Robotic Manipulation

Key points

• For example, a surface region of an object identified as graspable by a human annotator may be geometrically unreachable or mechanically infeasible for a robot, yet this mismatch is rarely addressed explicitly.1
• To tackle this challenge, we proposed a framework that translates human affordance annotations into robot-executable affordance predictions conditioned on the physical properties of the robot.1
• The network predicts which surface regions of an object are physically executable by the robot, given a 3D point cloud of an object and a descriptor encoding the robot's geometry and constraints.1
• The model was trained using human-annotated affordances together with robotic execution outcomes from multiple sampled approach trials in simulation, where execution outcomes determined which regions are executable for the robot.1

Sources (1)

• [1]Cross-Embodiment Affordance Transfer for Robotic Manipulation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 11:31 AM

  Robotic manipulation systems commonly learn object affordances from human-annotated datasets, which reflect interaction possibilities for a human hand rather than the physical constraints of a specific robot.

  For example, a surface region of an object identified as graspable by a human annotator may be geometrically unreachable or mechanically infeasible for a robot, yet this mismatch is rarely addressed explicitly.

Extractive summary: sentences quoted from the sources.