Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in the scene are known. To make such tools applicable for ego-centric planning with only local information, we propose an adaptive model-predictive game solver, which jointly infers other players' objectives online and computes a corresponding generalized Nash equilibrium (GNE) strategy. The adaptivity of our approach is enabled by a differentiable trajectory game solver whose gradient signal is used for maximum likelihood estimation (MLE) of opponents' objectives. This differentiability of our pipeline facilitates direct integration with other differentiable elements, such as neural networks (NNs). Furthermore, in contrast to existing solvers for cost inference in games, our method handles not only partial state observations but also general inequality constraints. In two simulated traffic scenarios, we find superior performance of our approach over both existing game-theoretic methods and non-game-theoretic model-predictive control (MPC) approaches. We also demonstrate our approach's real-time planning capabilities and robustness in two hardware experiments.

Vision-based tactile sensors have gained extensive attention in the robotics community. The sensors are highly expected to be capable of extracting contact information i.e. haptic information during in-hand manipulation. This nature of tactile sensors makes them a perfect match for haptic feedback applications. In this paper, we propose a contact force estimation method using the vision-based tactile sensor DIGIT, and apply it to a position-force teleoperation architecture for force feedback. The force estimation is done by building a depth map for DIGIT gel surface deformation measurement and applying a regression algorithm on estimated depth data and ground truth force data to get the depth-force relationship. The experiment is performed by constructing a grasping force feedback system with a haptic device as a leader robot and a parallel robot gripper as a follower robot, where the DIGIT sensor is attached to the tip of the robot gripper to estimate the contact force. The preliminary results show the capability of using the low-cost vision-based sensor for force feedback applications.

In 2019 Kerdels and Peters proposed a grid cell model (GCM) based on a Differential Growing Neural Gas (DGNG) network architecture as a computationally efficient way to model an Autoassociative Memory Cell (AMC) \cite{Kerdels_Peters_2019}. An important feature of the DGNG architecture with respect to possible applications in the field of computational neuroscience is its \textit{capacity} refering to its capability to process and uniquely distinguish input signals and therefore obtain a valid representation of the input space. This study evaluates the capacity of a two layered DGNG grid cell model on the Fashion-MNIST dataset. The focus on the study lies on the variation of layer sizes to improve the understanding of capacity properties in relation to network parameters as well as its scaling properties. Additionally, parameter discussions and a plausability check with a pixel/segment variation method are provided. It is concluded, that the DGNG model is able to obtain a meaningful and plausible representation of the input space and to cope with the complexity of the Fashion-MNIST dataset even at moderate layer sizes.

Recent NLP models have the great ability to generalise `zero-shot' to new tasks using only an instruction as guidance. However, these approaches usually repeat their instructions with every input, requiring costly reprocessing of lengthy instructions for every inference example. To alleviate this, we introduce Hypernetworks for INstruction Tuning (HINT), which convert task instructions and examples using a pretrained text encoder into parameter-efficient modules inserted into an underlying model, eliminating the need to include instructions in the model input. Compared to prior approaches that concatenate instructions with every input instance, we find that HINT models are significantly more compute-efficient and consistently outperform these approaches for a given inference budget.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

We consider a sequential decision making task where we are not allowed to evaluate parameters that violate an a priori unknown (safety) constraint. A common approach is to place a Gaussian process prior on the unknown constraint and allow evaluations only in regions that are safe with high probability. Most current methods rely on a discretization of the domain and cannot be directly extended to the continuous case. Moreover, the way in which they exploit regularity assumptions about the constraint introduces an additional critical hyperparameter. In this paper, we propose an information-theoretic safe exploration criterion that directly exploits the GP posterior to identify the most informative safe parameters to evaluate. Our approach is naturally applicable to continuous domains and does not require additional hyperparameters. We theoretically analyze the method and show that we do not violate the safety constraint with high probability and that we explore by learning about the constraint up to arbitrary precision. Empirical evaluations demonstrate improved data-efficiency and scalability.

Atrial Fibrillation (AF) is characterized by disorganised electrical activity in the atria and is known to be sustained by the presence of regions of fibrosis (scars) or functional cellular remodeling, both of which may lead to areas of slow conduction. Estimating the effective conductivity of the myocardium and identifying regions of abnormal propagation is therefore crucial for the effective treatment of AF. We hypothesise that the spatial distribution of tissue conductivity can be directly inferred from an array of concurrently acquired contact electrograms (EGMs). We generate a dataset of simulated cardiac AP propagation using randomised scar distributions and a phenomenological cardiac model and calculate contact electrograms at various positions on the field. A deep neural network, based on a modified U-net architecture, is trained to estimate the location of the scar and quantify conductivity of the tissue with a Jaccard index of $91$%. We adapt a wavelet-based surrogate testing analysis to confirm that the inferred conductivity distribution is an accurate representation of the ground truth input to the model. We find that the root mean square error (RMSE) between the ground truth and our predictions is significantly smaller ($p_{val}=0.007$) than the RMSE between the ground truth and surrogate samples.

We present hierarchical policy blending as optimal transport (HiPBOT). This hierarchical framework adapts the weights of low-level reactive expert policies, adding a look-ahead planning layer on the parameter space of a product of expert policies and agents. Our high-level planner realizes a policy blending via unbalanced optimal transport, consolidating the scaling of underlying Riemannian motion policies, effectively adjusting their Riemannian matrix, and deciding over the priorities between experts and agents, guaranteeing safety and task success. Our experimental results in a range of application scenarios from low-dimensional navigation to high-dimensional whole-body control showcase the efficacy and efficiency of HiPBOT, which outperforms state-of-the-art baselines that either perform probabilistic inference or define a tree structure of experts, paving the way for new applications of optimal transport to robot control. More material at https://sites.google.com/view/hipobot

PAC-Bayes has recently re-emerged as an effective theory with which one can derive principled learning algorithms with tight performance guarantees. However, applications of PAC-Bayes to bandit problems are relatively rare, which is a great misfortune. Many decision-making problems in healthcare, finance and natural sciences can be modelled as bandit problems. In many of these applications, principled algorithms with strong performance guarantees would be very much appreciated. This survey provides an overview of PAC-Bayes performance bounds for bandit problems and an experimental comparison of these bounds. Our experimental comparison has revealed that available PAC-Bayes upper bounds on the cumulative regret are loose, whereas available PAC-Bayes lower bounds on the expected reward can be surprisingly tight. We found that an offline contextual bandit algorithm that learns a policy by optimising a PAC-Bayes bound was able to learn randomised neural network polices with competitive expected reward and non-vacuous performance guarantees.

Linear-quadratic regulators (LQR) are a well known and widely used tool in control theory for both linear and nonlinear dynamics. For nonlinear problems, an LQR-based controller is usually only locally viable, thus, raising the problem of estimating the region of attraction (ROA). The need for good ROA estimations becomes especially pressing for underactuated systems, as a failure of controls might lead to unsafe and unrecoverable system states. Known approaches based on optimization or sampling, while working well, might be too slow in time critical applications and are hard to verify formally. In this work, we propose a novel approach to estimate the ROA based on the analytic solutions to linear ODEs for the torque limited simple pendulum. In simulation and physical experiments, we compared our approach to a Lyapunov-sampling baseline approach and found that our approach was faster to compute, while yielding ROA estimations of similar phase space area.