Model learning for trajectory tracking of robot manipulators

Abstract Model based controllers have drastically improved robot performance, increasing task accuracy while reducing control effort. Nevertheless, all this was realized with a very strong assumption: the exact knowledge of the physical properties of both the robot and the environment that surrounds it. This assertion is often misleading: in fact modern robots are modeled in a very approximate way and, more important, the environment is almost never static and completely known. Also for systems very simple, such as robot manipulators, these assumptions are still too strong and must be relaxed. Many methods were developed which, exploiting previous experiences, are able to refine the nominal model: from classic identification techniques to more modern machine learning based approaches. Indeed, the topic of this thesis is the investigation of these data driven techniques in the context of robot control for trajectory tracking. In the first two chapters, preliminary knowledge is provided on both model based controllers, used in robotics to assure precise trajectory tracking, and model learning techniques. In the following three chapters, are presented the novelties introduced by the author in this context with respect to the state of the art: three works with the same premise (an inaccurate system modeling), an identical goal (accurate trajectory tracking control) but with small differences according to the specific platform of application (fully actuated, underactuated, redundant robots). In all the considered architectures, an online learning scheme has been introduced to correct the nominal feedback linearization control law. Indeed, the method has been primarily introduced in the literature to cope with fully actuated systems, showing its efficacy in the accurate tracking of joint space trajectories also with an inaccurate dynamic model. The main novelty of the technique was the use of only kinematics information, instead of torque measurements (in general very noisy), to online retrieve and compensate the dynamic mismatches. After that the method has been extended to underactuated robots. This new architecture was composed by an online learning correction of the controller, acting on the actuated part of the system (the nominal partial feedback linearization), and an offline planning phase, required to realize a dynamically feasible trajectory also for the zero dynamics of the system. The scheme was iterative: after each trial, according to the collected information, both the phases were improved and then repeated until the task achievement. Also in this case the method showed its capability, both in numerical simulations and on real experiments on a robotics platform. Eventually the method has been applied to redundant systems: differently from before, in this context the task consisted in the accurate tracking of a Cartesian end effector trajectory. In principle very similar to the fully actuated case, the presence of redundancy slowed down drastically the learning machinery convergence, worsening the performance. In order to cope with this, a redundancy resolution was proposed that, exploiting an approximation of the learning algorithm (Gaussian process regression), allowed to locally maximize the information and so select the most convenient self motion for the system; moreover, all of this was realized with just the resolution of a quadratic programming problem. Also in this case the method showed its performance, realizing an accurate online tracking while reducing both the control effort and the joints velocity, obtaining so a natural behaviour. The thesis concludes with summary considerations on the proposed approach and with possible future directions of research

GEOLOGIA/ Quell\u27eruzione vulcanica che ha quasi cancellato la vita dalla Terra

not availabl

GEOLOGIA/ Risolto il giallo dei Trilobiti scomparsi: si era inceppata la pompa biologica oceanica

not availabl

Exploring the sequence length bottleneck in the Transformer for Image Captioning

Most recent state of the art architectures rely on combinations and variations of three approaches: convolutional, recurrent and self-attentive methods. Our work attempts in laying the basis for a new research direction for sequence modeling based upon the idea of modifying the sequence length. In order to do that, we propose a new method called "Expansion Mechanism" which transforms either dynamically or statically the input sequence into a new one featuring a different sequence length. Furthermore, we introduce a novel architecture that exploits such method and achieves competitive performances on the MS-COCO 2014 data set, yielding 134.6 and 131.4 CIDEr-D on the Karpathy test split in the ensemble and single model configuration respectively and 130 CIDEr-D in the official online evaluation server, despite being neither recurrent nor fully attentive. At the same time we address the efficiency aspect in our design and introduce a convenient training strategy suitable for most computational resources in contrast to the standard one. Source code is available at https://github.com/jchenghu/explorin

Magneto-transport in high g-factor, low-density two-dimensional electron systems confined in In_0.75Ga_0.25As/In_0.75Al_0.25As quantum wells

We report magneto-transport measurements on high-mobility two-dimensional electron systems (2DESs) confined in In_0.75Ga_0.25As/In_0.75Al_0.25As single quantum wells. Several quantum Hall states are observed in a wide range of temperatures and electron densities, the latter controlled by a gate voltage down to values of 1.10^11 cm^-2. A tilted-field configuration is used to induce Landau level crossings and magnetic transitions between quantum Hall states with different spin polarizations. A large filling factor dependent effective electronic g-factor is determined by the coincidence method and cyclotron resonance measurements. From these measurements the change in exchange-correlation energy at the magnetic transition is deduced. These results demonstrate the impact of many-body effects in tilted-field magneto-transport of high-mobility 2DESs confined in In_0.75Ga_0.25As/In_0.75Al_0.25As quantum wells. The large tunability of electron density and effective g-factor, in addition, make this material system a promising candidate for the observation of a large variety of spin-related phenomena.Comment: 7 pages, 5 figure

Work-in-Progress: Quantized NNs as the Definitive solution for inference on low-power ARM MCUs?

High energy efficiency and low memory footprint are the key requirements for the deployment of deep learning based analytics on low-power microcontrollers. Here we present work-in-progress results with Q-bit Quantized Neural Networks (QNNs) deployed on a commercial Cortex-M7 class microcontroller by means of an extension to the ARM CMSIS-NN library. We show that i) for Q=4 and Q=2 low memory footprint QNNs can be deployed with an energy overhead of 30% and 36% respectively against the 8-bit CMSIS-NN due to the lack of quantization support in the ISA; ii) for Q=1 native instructions can be used, yielding an energy and latency reduction of 3c3.8 7 with respect to CMSIS-NN. Our initial results suggest that a small set of QNN-related specialized instructions could improve performance by as much as 7.5 7 for Q=4, 13.6 7 for Q=2 and 6.5 7 for binary NNs

A RISC-V-based FPGA Overlay to Simplify Embedded Accelerator Deployment

Modern cyber-physical systems (CPS) are increasingly adopting heterogeneous systems-on-chip (HeSoCs) as a computing platform to satisfy the demands of their sophisticated workloads. FPGA-based HeSoCs can reach high performance and energy efficiency at the cost of increased design complexity. High-Level Synthesis (HLS) can ease IP design, but automated tools still lack the maturity to efficiently and easily tackle system-level integration of the many hardware and software blocks included in a modern CPS. We present an innovative hardware overlay offering plug-and-play integration of HLS-compiled or handcrafted acceleration IPs thanks to a customizable wrapper attached to the overlay interconnect and providing shared-memory communication to the overlay cores. The latter are based on the open RISC-V ISA and offer simplified software management of the acceleration IP. Deploying the proposed overlay on a Xilinx ZU9EG shows ≈ 20% LUT usage and ≈ 4× speedup compared to program execution on the ARM host core