Inferring geometric constraints in human demonstrations
This work addresses the challenge of interpreting human demonstrations for robotics, enabling more intuitive programming and task learning, though it appears incremental in its approach.
The paper tackles the problem of automatically inferring geometric constraints from human demonstrations without prior knowledge of constraint types or contact geometry, achieving robust inference using both kinematic and force/torque information.
This paper presents an approach for inferring geometric constraints in human demonstrations. In our method, geometric constraint models are built to create representations of kinematic constraints such as fixed point, axial rotation, prismatic motion, planar motion and others across multiple degrees of freedom. Our method infers geometric constraints using both kinematic and force/torque information. The approach first fits all the constraint models using kinematic information and evaluates them individually using position, force and moment criteria. Our approach does not require information about the constraint type or contact geometry; it can determine both simultaneously. We present experimental evaluations using instrumented tongs that show how constraints can be robustly inferred in recordings of human demonstrations.