Abstract Vibrotactile stimulation has been explored to accelerate the acquisition of motor skills involving finger movements (Markow et al., 2010; Seim et al., 2017; Gemicioglu et al., 2022). This study evaluates the effectiveness of vibrotactile stimulation compared to visual feedback in learning a 14-note one-handed tune on the piano. In the experiment, 14 subjects with no prior piano experience were exposed to both vibrotactile and visual stimulation to determine which was more effective. Subjects were randomized 1:1 in a group that first receives vibrotactile stimulation, then visual stimulation or in a group that first receives visual stimulation, then vibrotactile stimulation. Effectiveness was measured by evaluating the timing error and accuracy. Results from our study indicated that the timing error for vibrotactile stimulation was 12.1% (SD 6.0%), while the equivalent for visual stimulation was 22.3% (SD 10.3%). The accuracy for vibrotactile stimulation was 69.2% (SD 27.2%), while the equivalent for visual stimulation was 91.3% (SD 13.5%). It was observed that vibrotactile stimulation was generally more effective at minimizing the timing error at which the notes were hit compared to visual stimulation, and no statistically significant differences were found in accuracy (U = 61, p = .0930).

Abstract Motion capture is often retargeted to new, and sometimes drastically different, characters. When the characters take on realistic human shapes, however, we become more sensitive to the motion looking right. This means adapting it to be consistent with the physical constraints imposed by different body shapes. We show how to take realistic 3D human shapes, approximate them using a simplified representation, and animate them so that they move realistically using physically‐based retargeting. We develop a novel spacetime optimization approach that learns and robustly adapts physical controllers to new bodies and constraints. The approach automatically adapts the motion of the mocap subject to the body shape of a target subject. This motion respects the physical properties of the new body and every body shape results in a different and appropriate movement. This makes it easy to create a varied set of motions from a single mocap sequence by simply varying the characters. In an interactive environment, successful retargeting requires adapting the motion to unexpected external forces. We achieve robustness to such forces using a novel LQR‐tree formulation. We show that the simulated motions look appropriate to each character's anatomy and their actions are robust to perturbations.

Determining effective control strategies and solutions for high-degree-of-freedom humanoid characters has been a difficult, ongoing problem. A controller is only valid for a subset of the states of the character, known as the domain of attraction (DOA). This article shows how many states that are initially outside the DOA can be brought inside it. Our first contribution is to show how DOA expansion can be performed for a high-dimensional simulated character. Our second contribution is to present an algorithm that efficiently increases the DOA using random trees that provide denser coverage than the trees produced by typical sampling-based motion-planning algorithms. The trees are constructed offline but can be queried fast enough for near-real-time control. We show the effect of DOA expansion on getting up, crouch-to-stand, jumping, and standing-twist controllers. We also show how DOA expansion can be used to connect controllers together.

Abstract Synthesizing controllers for rotational movements in feature space is an open research problem and is particularly challenging because of the need to precisely regulate the character's global orientation, angular momentum and inertia. This paper presents feature‐based controllers for a wide variety of rotational movements, including cartwheels, dives and flips. We show that the controllers can be made robust to large external disturbances by using a time‐invariant control scheme. The generality of the control laws is demonstrated by providing examples of the flip controller with different apexes, the diving controller with different heights and styles, the cartwheel controller with different speeds and straddle widths, etc. The controllers do not rely on any input motion or offline optimization.

This paper presents the first method for full-body trajectory optimization of physics-based human motion that does not rely on motion capture, specified key-poses, or periodic motion. Optimization is performed using a small set of simple goals, for example, one hand should be on the ground, or the center-of-mass should be above a particular height. These objectives are applied to short spacetime windows which can be composed to express goals over an entire animation. Specific contact locations needed to achieve objectives are not required by our method. We show that the method can synthesize many different kinds of movement, including walking, hand walking, breakdancing, flips, and crawling. Most of these movements have never been previously synthesized by physics-based methods.