5 accepted papers
Movement primitives (MPs) are compact representations of robot skills that can be learned from demonstrations and combined into complex behaviors. However, merely equipping robots with a fixed set of innate MPs is insufficient to deploy them in dynamic and unpredictable environments. Instead, the fu…
Generating human-like robot motions is pivotal for achieving smooth human-robot interactions. Such motions contribute to better predictions of robot motions by humans, thus leading to more intuitive interaction and increased acceptability. Human likeness in robot motions has been conventionally meas…
Humans naturally execute many everyday manipulation actions with both arms simultaneously. Similarly, endowing robots with bimanual manipulation task models is key to efficiently perform complex manipulation tasks. To do so, a promising approach is to learn a library of task models from human demons…
The generation of energy-efficient and dynamic-aware robot motions that satisfy constraints such as joint limits, self-collisions, and collisions with the environment remains a challenge. In this context, Riemannian geometry offers promising solutions by identifying robot motions with geodesics on t…
Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear interactions between their degrees of freedom. However, these dynamical effects remain mostly overlooked when studying the mechanisms of human movement generation. Inspired by recent works, we hypothesize that huma…