  How to build AI that understands human intentions, and uses this knowledge to
collaborate with people? We describe a computational framework for evaluating
models of goal inference in the domain of 3D motor actions, which receives as
input the 3D coordinates of an agent's body, and of possible targets, to
produce a continuously updated inference of the intended target. We evaluate
our framework in three behavioural experiments using a novel Target Reaching
Task, in which human observers infer intentions of actors reaching for targets
among distracts. We describe Generative Body Kinematics model, which predicts
human intention inference in this domain using Bayesian inverse planning and
inverse body kinematics. We compare our model to three heuristics, which
formalize the principle of least effort using simple assumptions about the
actor's constraints, without the use of inverse planning. Despite being more
computationally costly, the Generative Body Kinematics model outperforms the
heuristics in certain scenarios, such as environments with obstacles, and at
the beginning of reaching actions while the actor is relatively far from the
intended target. The heuristics make increasingly accurate predictions during
later stages of reaching actions, such as, when the intended target is close,
and can be inferred by extrapolating the wrist trajectory. Our results identify
contexts in which inverse body kinematics is useful for intention inference. We
show that human observers indeed rely on inverse body kinematics in such
scenarios, suggesting that modeling body kinematic can improve performance of
inference algorithms.
