[1] Billard completed her PhD in 1998 and then moved back to Switzerland to pursue her postdoctoral studies at EPFL and The Swiss Artificial Intelligence Lab (IDSIA or Instituto Dalle Molle di Syudi sull’Intelligenza Artificiale) until 1999.

[5] She developed a system that was capable of learning simple syntactical language and she used two mobile and autonomous robots, acting as teacher and student, to implement the architecture.

[6] Billard suggested that social learning could be enhanced with more complex cognitive mechanisms that enable a robot to associate one outcome with a subsequent event instead of with simultaneous sensory.

[2] The main facets of research her laboratory currently explores are: human-robot interaction, machine learning with applications to robotics, fast adaptive control, dexterous manipulations and grasping, as well as computational neuroscience and cognitive modelling.

[10] Billard continued to explore more biologically inspired connectionist architectures with which to train robots to learn complex arm movements by imitation.

[11] Billard continues to base her computational approaches on biological systems and began to explore how implementing neuromodulatory mechanisms in neural networks produce tune-able pattern generation as they would in the human brain.

[12] Further, Billard and her colleagues began to implement neurobiological concepts such as homeostatic plasticity, Hebbian reinforcement learning, and hormone feedback into their neural networks to again provide adaptability and flexibility like that exists in the human brain.

[16][17] Billard and her team used golf putting as a task to explore the ability of the robot to learn complex motions and adapt to changes in position, speed, and target location.

[19][20] The video of their robot catching objects in flight has been viewed millions of times on YouTube and their publication in IEEE was the most frequently downloaded document in the journal.

[21] With 90% accuracy, they were able to decode three typical grasps, which provides a novel and effective approach to coordinating a subjects arm movements with a robotic hand to generate a natural pattern of motion.

[21] In 2016, Billard and her students won multiple awards for their paper 'Coordinated multi-arm motion planning: Reaching for moving objects in the face of uncertainty'.

They were able to validate their approach using a dual-arm robotic system and they found that it was able to adapt and coordinate the motion of each arm to catch flying objects at high speeds and with uncertainty in trajectory.

[23] Billard and her team have also implemented hierarchical knowledge systems to allow robots to learn both high-level complex task plans as well as lower level movements after demonstrations.