Solving Hard Control Problems in Voting Systems via Integer Programming

Voting problems are central in the area of social choice. In this article, we investigate various voting systems and types of control of elections. We present integer linear programming (ILP) formulations for a wide range of NP-hard control problems. Our ILP formulations are flexible in the sense that they can work with an arbitrary number of candidates and voters. Using the off-the-shelf solver Cplex, we show that our approaches can manipulate elections with a large number of voters and candidates efficiently

Two-Scale Thermomechanical Simulation of Hot Stamping

Hot stamping is a hot drawing process which takes advantage of the polymorphic steel behavior to produce parts with a good strength-to-weight ratio. For the simulation of the hot stamping process, a nonlinear two-scale thermomechanical model is suggested and implemented into the FE tool ABAQUS. Phase transformation and transformation induced plasticity effects are taken into account. The simulation results regarding the final shape and residual stresses are compared to experimental findings

Two-Scale Thermomechanical Simulation of Hot Stamping

Hot stamping is a hot drawing process which takes advantage of the polymorphic steel behavior to produce parts with a good strength-to-weight ratio. For the simulation of the hot stamping process, a nonlinear two-scale thermomechanical model is suggested and implemented into the FE tool ABAQUS. Phase transformation and transformation induced plasticity effects are taken into account. The simulation results regarding the final shape and residual stresses are compared to experimental findings

Diverse and rich fortified cultural heritage of the Iberian Peninsula. Basis for culture tourism with the European Culture Route Fortified Monuments FORTE CULTURA®

[EN] Fortresses are architectural pearls, cultural sites, event locations, experience places and memorials, mostly situated at breath-taking places on mountains, rivers or in the under-ground. Fortresses are monuments of common European history, they mirror the past into the present, connect cultures and offer deep insights into the historical conflicts. Fortified monuments are part of what makes Europe unique and attractive. This cultural heritage has to be preserved and made accessible for the culture tourism at the same time. The Iberian fortified heritage has big potential for new culture touristic topics and travel routes away from mass tourism. Therefore, cultural routes are a useful instrument. The European Culture Route Fortified Monuments –FORTE CULTURA®– is the European umbrella brand for fortress tourism. It offers useful instruments for international marketing of fortified monuments. The implementation of the attractive architectura militaris of the Iberian Peninsula into the culture route FORTE CULTURA® makes it possible to network this culture asset touristically, make it visible and experienceable on international tourism markets and market it Europe-wide. By implementing a new touristic regional brand “FORTE CULTURA – Iberian Fortified Heritage” the qualified culture tourism will be addressed. This supports a balance between over and under presented monuments and extends the sphere of activity of local actors onto whole Europe.Neumann, H.; Röder, D.; Röder, H. (2020). Diverse and rich fortified cultural heritage of the Iberian Peninsula. Basis for culture tourism with the European Culture Route Fortified Monuments FORTE CULTURA®. Editorial Universitat Politècnica de València. 971-976. https://doi.org/10.4995/FORTMED2020.2020.11394OCS97197

Probabilistic prioritization of movement primitives

Movement prioritization is a common approach to combine controllers of different tasks for redundant robots, where each task is assigned a priority. The priorities of the tasks are often hand-tuned or the result of an optimization, but seldomly learned from data. This paper combines Bayesian task prioritization with probabilistic movement primitives to prioritize full motion sequences that are learned from demonstrations. Probabilistic movement primitives (ProMPs) can encode distributions of movements over full motion sequences and provide control laws to exactly follow these distributions. The probabilistic formulation allows for a natural application of Bayesian task prioritization. We extend the ProMP controllers with an additional feedback component that accounts inaccuracies in following the distribution and allows for a more robust prioritization of primitives. We demonstrate how the task priorities can be obtained from imitation learning and how different primitives can be combined to solve even unseen task-combinations. Due to the prioritization, our approach can efficiently learn a combination of tasks without requiring individual models per task combination. Further, our approach can adapt an existing primitive library by prioritizing additional controllers, for example, for implementing obstacle avoidance. Hence, the need of retraining the whole library is avoided in many cases. We evaluate our approach on reaching movements under constraints with redundant simulated planar robots and two physical robot platforms, the humanoid robot “iCub” and a KUKA LWR robot arm

ProDMPs: A Unified Perspective on Dynamic and Probabilistic Movement Primitives

Movement Primitives (MPs) are a well-known concept to represent and generate modular trajectories. MPs can be broadly categorized into two types: (a) dynamics-based approaches that generate smooth trajectories from any initial state, e. g., Dynamic Movement Primitives (DMPs), and (b) probabilistic approaches that capture higher-order statistics of the motion, e. g., Probabilistic Movement Primitives (ProMPs). To date, however, there is no method that unifies both, i. e. that can generate smooth trajectories from an arbitrary initial state while capturing higher-order statistics. In this paper, we introduce a unified perspective of both approaches by solving the ODE underlying the DMPs. We convert expensive online numerical integration of DMPs into basis functions that can be computed offline. These basis functions can be used to represent trajectories or trajectory distributions similar to ProMPs while maintaining all the properties of dynamical systems. Since we inherit the properties of both methodologies, we call our proposed model Probabilistic Dynamic Movement Primitives (ProDMPs). Additionally, we embed ProDMPs in deep neural network architecture and propose a new cost function for efficient end-to-end learning of higher-order trajectory statistics. To this end, we leverage Bayesian Aggregation for non-linear iterative conditioning on sensory inputs. Our proposed model achieves smooth trajectory generation, goal-attractor convergence, correlation analysis, non-linear conditioning, and online re-planing in one framework.Comment: 12 pages, 13 figure

MP3: Movement Primitive-Based (Re-)Planning Policy

We introduce a novel deep reinforcement learning (RL) approach called Movement Prmitive-based Planning Policy (MP3). By integrating movement primitives (MPs) into the deep RL framework, MP3 enables the generation of smooth trajectories throughout the whole learning process while effectively learning from sparse and non-Markovian rewards. Additionally, MP3 maintains the capability to adapt to changes in the environment during execution. Although many early successes in robot RL have been achieved by combining RL with MPs, these approaches are often limited to learning single stroke-based motions, lacking the ability to adapt to task variations or adjust motions during execution. Building upon our previous work, which introduced an episode-based RL method for the non-linear adaptation of MP parameters to different task variations, this paper extends the approach to incorporating replanning strategies. This allows adaptation of the MP parameters throughout motion execution, addressing the lack of online motion adaptation in stochastic domains requiring feedback. We compared our approach against state-of-the-art deep RL and RL with MPs methods. The results demonstrated improved performance in sophisticated, sparse reward settings and in domains requiring replanning.Comment: The video demonstration can be accessed at https://intuitive-robots.github.io/mp3_website/. arXiv admin note: text overlap with arXiv:2210.0962

Curriculum-Based Imitation of Versatile Skills

Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelihood given the demonstrations. Yet, human demonstrations are often multi-modal, i.e., the same task is solved in multiple ways which is a major challenge for most imitation learning methods that are based on such a maximum likelihood (ML) objective. The ML objective forces the model to cover all data, it prevents specialization in the context space and can cause mode-averaging in the behavior space, leading to suboptimal or potentially catastrophic behavior. Here, we alleviate those issues by introducing a curriculum using a weight for each data point, allowing the model to specialize on data it can represent while incentivizing it to cover as much data as possible by an entropy bonus. We extend our algorithm to a Mixture of (linear) Experts (MoE) such that the single components can specialize on local context regions, while the MoE covers all data points. We evaluate our approach in complex simulated and real robot control tasks and show it learns from versatile human demonstrations and significantly outperforms current SOTA methods. A reference implementation can be found at https://github.com/intuitive-robots/ml-cu