publications 

​Over the years, there have been many improvements in job-related safety standards and working conditions, but there are still many situations and environments where human lives are put at risk, such as in search and rescue situations, construction sites, and chemical plants. We envision a world where robots can act as physical avatars and effectively replace humans in those hazardous scenarios through teleoperation.

Purpose of review: Humanoid robots are versatile platforms
with the potential to assist humans in several domains, from edu-
cation to healthcare, from entertainment to the factory of the
future. To find their place into our daily life, where complex
interactions and collaborations with humans are expected, their
social and physical interaction skills need to be further improved.
Recent findings: The hallmark of humanoids is their anthropomorphic
shape, which facilitates the interaction but at the same time increases
the expectations of the human in terms of advanced cooperation capa-
bilities. Cooperation with humans requires an appropriate modeling and
real-time estimation of the human state and intention. This informa-
tion is required both at a high-level by the cooperative decision-making
policy and at a low-level by the interaction controller that implements
the physical interaction. Real-time constraints induce simplified models
that limit the decision capabilities of the robot during cooperation.

​Summary: In this article, we review the current achievements in the
context of human-humanoid interaction and cooperation. We report on
the cognitive and cooperation skills that the robot needs to help humans
achieve their goals, and how these high-level skills translate into the
robot’s low-level control commands. Finally, we report on the applica-
tions of humanoid robots as humans’ companions, co-workers or avatars.

Generating complex movements in redundant robots like humanoids is usually done by means of multi-task controllers based on quadratic programming, where a multitude of tasks is organized according to strict or soft priorities. Time-consuming tuning and expertise are required to choose suitable task priorities, and to optimize their gains. Here, we automatically learn the controller configuration (soft and strict task priorities and Convergence Gains), looking for solutions that track a variety of desired task trajectories efficiently while preserving the robot's balance. We use multi-objective optimization to compare and choose among Pareto-optimal solutions that represent a trade-off of performance and robustness and can be transferred onto the real robot. We experimentally validate our method by learning a control configuration for the iCub humanoid, to perform different whole-body tasks, such as picking up objects …

Transferring the motion from a human operator to a humanoid robot is a crucial step to enable robots to learn from and replicate human movements. The ability to retarget in realtime whole-body motions that are challenging for the humanoid balance is critical to enable human to humanoid teleoperation. In this work, we design a retargeting framework that allows the robot to replicate the motion of the human operator, acquired by a wearable motion capture suit, while maintaining the whole-body balance. We introduce some dynamic filter in the retargeting to forbid dangerous motions that can make the robot fall. We validate our approach through several experiments on the iCub robot, which has a significantly different body structure and size from the one of the human operator.

Motion retargeting and teleoperation are powerful tools to demonstrate complex whole-body movements to humanoid robots: in a sense, they are the equivalent of kinesthetic teaching for manipulators. However, retargeted motions may not be optimal for the robot: because of different kinematics and dynamics, there could be other robot trajectories that perform the same task more efficiently, for example with less power consumption. We propose to use the retargeted trajectories to bootstrap a learning process aimed at optimizing the whole-body trajectories w.r.t. a specified cost function. To ensure that the optimized motions are safe, i.e., they do not violate system constraints, we use constrained optimization algorithms. We compare both global and local optimization approaches, since the optimized robot solution may not be close to the demonstrated one. We evaluate our framework with the humanoid robot iCub …