Skid-Steer Wheeled Mobile Robots (SSWMRs) are increasingly being used for off-road autonomy applications. When turning at high speeds, these robots tend to undergo significant skidding and slipping. In this work, using Gaussian Process Regression (GPR) and Sigma-Point Transforms, we estimate the non-linear effects of tire-terrain interaction on robot velocities in a probabilistic fashion. Using the mean estimates from GPR, we propose a data-driven dynamic motion model that is more accurate at predicting future robot poses than conventional kinematic motion models. By efficiently solving a convex optimization problem based on the history of past robot motion, the GPR augmented motion model generalizes to previously unseen terrain conditions. The output distribution from the proposed motion model can be used for local motion planning approaches, such as stochastic model predictive control, leveraging model uncertainty to make safe decisions. We validate our work on a benchmark real-world multi-terrain SSWMR dataset. Our results show that the model generalizes to three different terrains while significantly reducing errors in linear and angular motion predictions. As shown in the attached video, we perform a separate set of experiments on a physical robot to demonstrate the robustness of the proposed algorithm.

Conventional bidirectional conveyance platforms use a flat translating belt or a series of spinning wheels or rollers to apply a shear force to payloads to move them. Wheel/roller-based conveyors in particular cannot double as a worktop when idle, do not support collision-free multi-object manipulation by default, and are not optimized to move objects that are either slippery or pliable—let alone both. This paper introduces a Voxel-Enabled Robotic Assistant (VERA), a network of intelligent table "partitions" whose topologically dynamic worktops enable omnidirectional conveyance; each partition is composed of a 2D array of "quadrants," axisymmetric modules that can be hot-swapped for maintenance or repairs; each quadrant contains a 2D array of "cells," unitary robotic submodules; each cell houses an independently controllable "voxel," the motorized rotary element that conveys an overhead object. The efficacy of a VERA prototype was determined by evaluating waypoint error as a range of payloads were maneuvered between trajectory waypoints. By conveying both pliable and rigid payloads having slippery textures, the faceted voxels outperformed those augmented to mimic the circular-profiled wheels/rollers of competitor systems. VERA also successfully performed collision-free multi-object planar manipulations planned by its pathfinding algorithm. In light of these results, VERA emerges as a promising material handling platform for use in "Future of Work" settings as the need for multi-purpose collaborative industrial robots continues to grow.

Humans are skillful at manipulating objects that possess nonlinear underactuated dynamics, such as clothes or containers filled with liquids. Several studies suggested that humans implement a predictive model-based strategy to control such objects. However, these studies only considered unconstrained reaching without any object involved or, at most, linear mass-spring systems with relatively simple dynamics. It is not clear what internal model humans develop of more complex objects, and what level of granularity is represented. To answer these questions, this study examined a task where participants physically interacted with a nonlinear underactuated system mimicking a cup of sloshing coffee: a cup with a ball rolling inside. The cup and ball were simulated in a virtual environment and subjects interacted with the system via a haptic robotic interface. Participants were instructed to move the system and arrive at a target region with both cup and ball at rest, 'zeroing out' residual oscillations of the ball. This challenging task affords a solution known as 'input shaping', whereby a series of pulses moves the dynamic object to the target leaving no residual oscillations. Since the timing and amplitude of these pulses depend on the controller's internal model of the object, input shaping served as a tool to identify the subjects' internal representation of the cup-and-ball. Five simulations with different internal models were compared against the human data. Results showed that the features in the data were correctly predicted by a simple internal model that represented the cup-and-ball as a single rigid mass coupled to the hand impedance. These findings provide evidence that humans use simplified internal models along with mechanical impedance to manipulate complex objects.

Tactile sensing has been increasingly utilized in robot control of unknown objects to infer physical properties and optimize manipulation. However, there is limited understanding about the contribution of different sensory modalities during interactive perception in complex interaction both in robots and in humans. This study investigated the effect of visual and haptic information on humans' exploratory interactions with a ‘cup of coffee’, an object with nonlinear internal dynamics. Subjects were instructed to rhythmically transport a virtual cup with a rolling ball inside between two targets at a specified frequency, using a robotic interface. The cup and targets were displayed on a screen, and force feedback from the cup-and-ball dynamics was provided via the robotic manipulandum. Subjects were encouraged to explore and prepare the dynamics by “shaking” the cup-and-ball system to find the best initial conditions prior to the task. Two groups of subjects received the full haptic feedback about the cup-and-ball movement during the task; however, for one group the ball movement was visually occluded. Visual information about the ball movement had two distinctive effects on the performance: it reduced preparation time needed to understand the dynamics and, importantly, it led to simpler, more linear input-output interactions between hand and object. The results highlight how visual and haptic information regarding nonlinear internal dynamics have distinct roles for the interactive perception of complex objects.

Humans are adept at a wide variety of motor skills, including the handling of complex objects and using tools. Advances to understand the control of voluntary goal-directed movements have focused on simple behaviors such as reaching, uncoupled to any additional object dynamics. Under these simplified conditions, basic elements of motor control, such as the roles of body mechanics, objective functions, and sensory feedback, have been characterized. However, these elements have mostly been examined in isolation, and the interactions between these elements have received less attention. This study examined a task with internal dynamics, inspired by the daily skill of transporting a cup of coffee, with additional expected or unexpected perturbations to probe the structure of the controller. Using optimal feedback control (OFC) as the basis, it proved necessary to endow the model of the body with mechanical impedance to generate the kinematic features observed in the human experimental data. The addition of mechanical impedance revealed that simulated movements were no longer sensitively dependent on the objective function, a highly debated cornerstone of optimal control. Further, feedforward replay of the control inputs was similarly successful in coping with perturbations as when feedback, or sensory information, was included. These findings suggest that when the control model incorporates a representation of the mechanical properties of the limb, that is, embodies its dynamics, the specific objective function and sensory feedback become less critical, and complex interactions with dynamic objects can be successfully managed.

Collaborative robots that physically interact with humans in an ergonomic and safe manner are essential to the future of industry. A common task across many industrial applications is robot-to-human handover, in which the location of object exchange is vital in cultivating a seamless interaction. Most prior work on computing these exchange locations aims to adjust human posture towards a better ergonomic state during a single handover. This procedure typically involves the robot estimating the human’s biomechanical properties, e.g. center of mass and base of support, before determining an optimal handover location according to some ergonomics assessment scale. In a similar vein, we compare two methodologies for object handover, whereby the handover location is computed to either "assist" or "stimulate" the human receiver. Unlike existing approaches, we posit that improvements in human posture can be derived by stimulating the receiver’s movement dynamics to facilitate posture variability, rather than constrain or stabilize it. To compare methodologies, we conduct a within-subjects study where participants perform 78 object handovers with a collaborative robot architecture. Our ndings indicate an improvement in ergonomics scores for the "stimulating" approach, hinting at the importance of productive inconvenience in long-term robot-to-human handover.

Modern computer algorithms easily beat world champions in chess or Go, but state-of-the-art robots are still outperformed by two-year-old’s in manipulating the pieces, let alone interacting with more complex objects. This work studied human behavior when moving an underactuated object, a cup with a ball rolling inside creating internal dynamics like sloshing coffee in a cup. The objective was to develop a control model that could replicate human behavior. Human movement data were collected for transporting this cup-and-ball system, both with and without external perturbations. The existing models in the human control literature, including maximum smoothness, optimal feedback control with minimum effort, and dynamic primitives with impedance were revisited for this challenging task. As these control models were primarily developed for unconstrained reaching movements, they could replicate human trajectories when transporting a rigid object. However, they fell short when the object introduced complex interaction forces due to its internal dynamics. Therefore, this study extended the framework of dynamic primitives and used an optimal controller to generate a maximally smooth zero-force trajectory for the impedance operator when interacting with perturbations from the object or the environment. Given the challenges that robot control still faces when interacting with complex objects, these findings may inform the development of bio-inspired controllers for robotic manipulation.

Humans dexterously interact with a variety of objects, including those with complex internal dynamics. Even in the simple action of carrying a cup of coffee, the hand not only applies a force to the cup, but also indirectly to the liquid, which elicits complex reaction forces back on the hand. Due to underactuation and nonlinearity, the object's dynamic response to an action sensitively depends on its initial state and can display unpredictable, even chaotic behavior. With the overarching hypothesis that subjects strive for predictable object-hand interactions, this study examined how subjects explored and prepared the dynamics of an object for subsequent execution of the target task. We specifically hypothesized that subjects find initial conditions that shorten the transients prior to reaching a stable and predictable steady state. Reaching a predictable steady state is desirable as it may reduce the need for online error corrections and facilitate feed forward control. Alternative hypotheses were that subjects seek to reduce effort, increase smoothness, and reduce risk of failure. Motivated by the task of 'carrying a cup of coffee', a simplified cup-and-ball model was implemented in a virtual environment. Human subjects interacted with this virtual object via a robotic manipulandum that provided force feedback. Subjects were encouraged to first explore and prepare the cup-and-ball before initiating a rhythmic movement at a specified frequency between two targets without losing the ball. Consistent with the hypotheses, subjects increased the predictability of interaction forces between hand and object and converged to a set of initial conditions followed by significantly decreased transients. The three alternative hypotheses were not supported. Surprisingly, the subjects' strategy was more effortful and less smooth, unlike the observed behavior in simple reaching movements. Inverse dynamics of the cup-and-ball system and forward simulations with an impedance controller successfully described subjects' behavior. The initial conditions chosen by the subjects in the experiment matched those that produced the most predictable interactions in simulation. These results present first support for the hypothesis that humans prepare the object to minimize transients and increase stability and, overall, the predictability of hand-object interactions.

Relatively little work in human and robot control has examined the control of underactuated objects with internal dynamics, such as transporting a cup of coffee, a task that presents little problems for humans. This study examined how humans move a `cup of coffee' with a view to identify principles that may be useful for robot control. The specific focus was on how humans choose initial conditions to safely reach a steady state. We hypothesized that subjects choose initial conditions that minimized the transient duration to reach the steady state faster, as it presented more predictable dynamics. In the experiment, the cup of coffee was reduced to a 2-D cup with a sliding ball inside which was simulated in a virtual environment. Human subjects interacted with this virtual object via a robotic manipulandum that provided haptic feedback. Participants moved the cup between two targets without losing the ball; they were instructed to explore different initial conditions before initiating the continuous interaction. Results showed that subjects converged to a small set of initial conditions that decreased their transient durations and achieved a predictable steady state faster. Simulations with a simple feedforward controller and inverse dynamics calculations confirmed that these initial conditions indeed led to shorter transients and less complex interaction forces. These results may inform robot control of objects with internal dynamics where the effects of initial conditions need further investigation.

This study examined strategies humans chose to manipulate an object with complex (nonlinear, underactuated) dynamics, such as liquid sloshing in a cup of coffee. The problem was simplified to the well-known cart-and-pendulum system moving on a horizontal line. This model was implemented in a virtual environment and human subjects manipulated the object via a robotic manipulandum. The task was to maneuver the system from rest to arrive at a target position such that no residual oscillations of the pendulum bob remained. Our goal was to test whether humans simplified control by employing dynamic primitives, specifically submovements. Experimental velocity profiles of the human movements were compared to those predicted by three different control models. Two models used continuous optimization-based control, the third control model was based on Input Shaping. Input Shaping is a method for controlling flexible objects by convolving a motion profile with impulses of appropriate amplitude and timing. To evaluate whether humans used Input Shaping, we decomposed the velocity profiles recorded from humans into submovements, as proxies for the convolved impulses. Comparing the motion profiles from the 3 models with the experimentally measured human profiles showed superior performance of the Input Shaping model. These initial results are consistent with our hypothesis that combining dynamic primitives, submovements, is a competent description of human performance and may provide a simpler alternative to computationally complex optimization-based methods of robot control.

This study examines human control of physical interaction with objects that exhibit complex (nonlinear, chaotic, underactuated) dynamics. We hypothesized that humans exploited stability properties of the human-object interaction. Using a simplified 2D model for carrying a "cup of coffee", we developed a virtual implementation to identify human control strategies. Transporting a cup of coffee was modeled as a cart with a suspended pendulum, where humans moved the cart on a horizontal line via a robotic manipulandum. The specific task was to transport the cart-pendulum system to a target, as fast as possible, while accommodating assistive and resistive perturbations. To assess trajectory stability, we applied contraction analysis. We showed that when the perturbation was assistive, humans absorbed the perturbation by controlling cart trajectories into a contraction region prior to the perturbation. When the perturbation was resistive, subjects passed through a contraction region following the perturbation. Entering a contraction region stabilizes performance and makes the dynamics more predictable. This human control strategy could inspire more robust control strategies for physical interaction in robots.