No abstract

The development of robots that can dance has received considerable attention. However, they are often either limited to a pre-defined set of movements and music or demonstrate little variance when reacting to external stimuli, such as microphone or camera input. In this paper, we contribute with a novel approach allowing a legged robot to listen to live music while dancing in synchronization with the music in a diverse fashion. This is achieved by extracting the beat from an onboard microphone in real-time, and subsequently creating a dance choreography by picking from a user-generated dance motion library at every new beat. Dance motions include various stepping and base motions. The process of picking from the library is defined by a probabilistic model, namely a Markov chain, that depends on the previously picked dance motion and the current music tempo. Finally, delays are determined online by time-shifting a measured signal and a reference signal, and minimizing the least squares error with the time-shift as parameter. Delays are then compensated for by using a combined feedforward and feedback delay controller which shifts the robot whole-body controller reference input in time. Results from experiments on a quadrupedal robot demonstrate the fast convergence and synchrony to the perceived music.

Data-driven approaches to tactile sensing aim to overcome the complexity of accurately modeling contact with soft materials. However, their widespread adoption is impaired by concerns about data efficiency and the capability to generalize when applied to various tasks. This paper focuses on both these aspects with regard to a vision-based tactile sensor, which aims to reconstruct the distribution of the threedimensional contact forces applied on its soft surface. Accurate models for the soft materials and the camera projection, derived via state-of-the-art techniques in the respective domains, are employed to generate a dataset in simulation. A strategy is proposed to train a tailored deep neural network entirely from the simulation data. The resulting learning architecture is directly transferable across multiple tactile sensors without further training and yields accurate predictions on real data, while showing promising generalization capabilities to unseen contact conditions.

This letter aims to show that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the objects to be manipulated. For this purpose, a robotic system is presented that is able to swing up poles of different masses, radii and lengths, to an angle of 180 • , while relying solely on the feedback provided by the tactile sensor. This is achieved by developing a novel simulator that accurately models the interaction of a pole with the soft sensor. A feedback policy that is conditioned on a sensory observation history, and which has no prior knowledge of the physical features of the pole, is then learned in the aforementioned simulation. When evaluated on the physical system, the policy is able to swing up a wide range of poles that differ significantly in their physical attributes without further adaptation. To the authors' knowledge, this is the first work where a feedback policy from high-dimensional tactile observations is used to control the swing-up manipulation of poles in closed-loop.