We propose a new metric for robot state estimation based on the recently introduced SE2(3) Lie group definition. Our metric is related to prior metrics for SLAM but explicitly takes into account the linear velocity of the state estimate, improving over current pose-based trajectory analysis. This has the benefit of providing a single, quantitative metric to evaluate state estimation algorithms against, while being compatible with existing tools and libraries. Since ground truth data generally consists of pose data from motion capture systems, we also propose an approach to compute the ground truth linear velocity based on polynomial interpolation. Using Chebyshev interpolation and a pseudospectral parameterization, we can accurately estimate the ground truth linear velocity of the trajectory in an optimal fashion with best approximation error. We demonstrate how this approach performs on multiple robotic platforms where accurate state estimation is vital, and compare it to alternative approaches such as finite differences. The pseudospectral parameterization also provides a means of trajectory data compression as an additional benefit. Experimental results show our method provides a valid and accurate means of comparing state estimation systems, which is also easy to interpret and report.

We propose a manifold optimization approach for solving constrained inference and planning problems. The approach employs a framework that transforms an arbitrary nonlinear equality constrained optimization problem into an unconstrained manifold optimization problem. The core of the transformation process is the formulation of constraint manifolds that represent sets of variables subject to equality constraints. We propose various approaches to define the tan-gent spaces and retraction operations of constraint manifolds, which are crucial for manifold optimization. We evaluate our constraint manifold optimization approach on multiple constrained inference and planning problems, and show that it generates strictly feasible results with increased efficiency as compared to state-of-the-art constrained optimization methods.

The pace of advancement of the top-end supercomputers historically followed an exponential curve similar to (and driven in part by) Moore's Law. Shortly after hitting the petaflop mark, the community started looking ahead to the next milestone: Exascale. However, many obstacles were already looming on the horizon, such as the slowing of Moore's Law, and others like the end of Dennard Scaling had already arrived. Anticipating significant challenges for the overall high-performance computing (HPC) community to achieve the next 1000x improvement, the U.S. Department of Energy (DOE) launched the Exascale Computing Program to enable and accelerate fundamental research across the many technologies needed to achieve exascale computing.

We present a novel continuous time trajectory representation based on a Chebyshev polynomial basis, which when governed by known dynamics models, allows for full trajectory and robot dynamics estimation, particularly useful for high-performance robotics applications such as unmanned aerial vehicles. We show that we can gracefully incorporate model dynamics to our trajectory representation, within a factor-graph based framework, and leverage ideas from pseudo- spectral optimal control to parameterize the state and the control trajectories as interpolating polynomials. This allows us to perform efficient optimization at specifically chosen points derived from the theory, while recovering full trajectory estimates. Through simulated experiments we demonstrate the applicability of our representation for accurate flight dynamics estimation for multirotor aerial vehicles. The representation framework is general and can thus be applied to a multitude of high-performance applications beyond multirotor platforms.

In this paper, we investigate deep image synthesis guided by sketch, color, and texture. Previous image synthesis methods can be controlled by sketch and color strokes but we are the first to examine texture control. We allow a user to place a texture patch on a sketch at arbitrary locations and scales to control the desired output texture. Our generative network learns to synthesize objects consistent with these texture suggestions. To achieve this, we develop a local texture loss in addition to adversarial and content loss to train the generative network. We conduct experiments using sketches generated from real images and textures sampled from a separate texture database and results show that our proposed algorithm is able to generate plausible images that are faithful to user controls. Ablation studies show that our proposed pipeline can generate more realistic images than adapting existing methods directly.

All software in use today relies on libraries, including standard libraries (e.g., C, C++) and application-specific libraries (e.g., libxml, libpng). Most libraries are loaded in memory and dynamically linked when programs are launched, resolving symbol addresses across the applications and libraries. Dynamic linking has many benefits: It allows code to be reused between applications, conserves memory (because only one copy of a library is kept in memory for all the applications that share it), and allows libraries to be patched and updated without modifying programs, among numerous other benefits. However, these benefits come at the cost of performance. For every call made to a function in a dynamically linked library, a trampoline is used to read the function address from a lookup table and branch to the function, incurring memory load and branch operations. Static linking avoids this performance penalty, but loses all the benefits of dynamic linking. Given its myriad benefits, dynamic linking is the predominant choice today, despite the performance cost. In this work, we propose a speculative hardware mechanism to optimize dynamic linking by avoiding executing the trampolines for library function calls, providing the benefits of dynamic linking with the performance of static linking. Speculatively skipping the memory load and branch operations of the library call trampolines improves performance by reducing the number of executed instructions and gains additional performance by reducing pressure on the instruction and data caches, TLBs, and branch predictors. Because the indirect targets of library call trampolines do not change during program execution, our speculative mechanism never misspeculates in practice. We evaluate our technique on real hardware with production software and observe up to 4% speedup using only 1.5KB of on-chip storage.

Cable conduit actuation provides a simple yet dexterous mode of power transmission for remote actuation. However, they are not preferred because of the nonlinearities arising from friction and cable compliance which lead to backlash type of behavior. Unlike most of the current research in backlash control which generally assumes no knowledge of one of the intermediate states, the controller design in this case can be significantly simplified if output feedback of the system is available. This paper uses a simple feedforward control law for backlash compensation. A novel smooth backlash inverse is proposed, which takes the physical limitations of the actuator in consideration, unlike other designs, and thus makes it more intuitive to use. Implementation of this inverse on physical systems can also improve the system performance over the theoretical exact inverse, as well as other existing smooth inverse designs. Improvement in the performance is shown through experiments on a robot arm of Laprotek surgical system as well as on an experimental setup using polymeric cables for actuation.

Many robotic systems, like surgical robots, robotic hands, and exoskeleton robots, use cable passing through conduits to actuate remote instruments. Cable actuation simplifies the design and allows the actuator to be located at a convenient location, away from the end effector. However, nonlinear frictions between the cable and the conduit account for major losses in tension transmission across the cable, and a model is needed to characterize their effects in order to analyze and compensate for them. Although some models have been proposed in the literature, they are lumped parameter based and restricted to the very special case of a single cable with constant conduit curvature and constant pretension across the cable only. This paper proposes a mathematically rigorous distributed parameter model for cable-conduit actuation with any curvature and initial tension profile across the cable. The model, which is described by a set of partial differential equations in the continuous time-domain, is also discretized for the effective numerical simulation of the cable motion and tension transmission across the cable. Unlike the existing lumped-parameter-based models, the resultant discretized model enables one to accurately simulate the partial-moving/partial-sticking cable motion of the cable-conduit actuation with any curvature and initial tension profile. The model is further extended to cable-conduit actuation in pull-pull configuration using a pair of cables. Various simulations results are presented to reveal the unique phenomena like backlash, cable slacking, interaction between the two cables, and other nonlinear behaviors associated with the cable conduits in pull-pull configuration. These results are verified by experiments using two dc motors coupled with a cable-conduit pair. The experimental setup has been prepared to emulate a typical cable-actuated robotic system. Experimental results are compared with the simulations and various implications are discussed.

Many surgical robots use cable-conduit pairs in a pull-pull configuration to actuate the instruments and transmit power into the patient’s body. Friction between the cable and the conduit makes the system nonlinear and accounts for major losses in tension transmission across the cable. This paper proposes an analytical model for a similar cable-conduit system and formulates the load transmission characteristics. The dynamic model uses discrete elements with friction losses and cable stretch calculated for each of the segments. The simulations predict backlash, cable slacking, and other nonlinear behavior. These results are verified with an experiment using two DC motors coupled with a cable-conduit pair. The drive motor is run in position control mode, while the load motor simulates a passive environment torsional spring. Experimental results are compared with the simulation and various implications are discussed.