Autonomous vehicles (AVs) need to reason about the multimodal behavior of neighboring agents while planning their own motion. Many existing trajectory planners seek a single trajectory that performs well under \emph{all} plausible futures simultaneously, ignoring bi-directional interactions and thus leading to overly conservative plans. Policy planning, whereby the ego agent plans a policy that reacts to the environment's multimodal behavior, is a promising direction as it can account for the action-reaction interactions between the AV and the environment. However, most existing policy planners do not scale to the complexity of real autonomous vehicle applications: they are either not compatible with modern deep learning prediction models, not interpretable, or not able to generate high quality trajectories. To fill this gap, we propose Tree Policy Planning (TPP), a policy planner that is compatible with state-of-the-art deep learning prediction models, generates multistage motion plans, and accounts for the influence of ego agent on the environment behavior. The key idea of TPP is to reduce the continuous optimization problem into a tractable discrete Markov Decision Process (MDP) through the construction of two tree structures: an ego trajectory tree for ego trajectory options, and a scenario tree for multi-modal ego-conditioned environment predictions. We demonstrate the efficacy of TPP in closed-loop simulations based on real-world nuScenes dataset and results show that TPP scales to realistic AV scenarios and significantly outperforms non-policy baselines.

This paper presents a convex-QCQP based novel path planning algorithm named ellipsoidal constrained agent navigation (ECAN), for a challenging problem of online path planning in completely unknown and unseen continuous environments. ECAN plans path for the agent by making a tunnel of overlapping ellipsoids, in an online fashion, through the environment. Convex constraints in the ellipsoid-formation step circumvent collision with the obstacles. The problem of online-tunneling is solved as a convex-QCQP. This paper assumes no constraints on shape of the agent and the obstacles. However, to make the approach clearer, this paper first introduces the framework for a point-mass agent with point-size obstacles. After explaining the underlying principle in drawing an ellipsoid tunnel, the framework is extended to the agent and obstacles having finite area (2d space) and finite-volume (3d-space).

Recent decades have witnessed great advancements in multiobjective evolutionary algorithms (MOEAs) for multiobjective optimization problems (MOPs). However, these progressively improved MOEAs have not necessarily been equipped with scalable and learnable problem-solving strategies for new and grand challenges brought by the scaling-up MOPs with continuously increasing complexity from diverse aspects, mainly including expensive cost of function evaluations, many objectives, large-scale search space, time-varying environments, and multi-task. Under different scenarios, divergent thinking is required in designing new powerful MOEAs for solving them effectively. In this context, research studies on learnable MOEAs with machine learning techniques have received extensive attention in the field of evolutionary computation. This paper begins with a general taxonomy of scaling-up MOPs and learnable MOEAs, followed by an analysis of the challenges that these MOPs pose to traditional MOEAs. Then, we synthetically overview recent advances of learnable MOEAs in solving various scaling-up MOPs, focusing primarily on four attractive directions (i.e., learnable evolutionary discriminators for environmental selection, learnable evolutionary generators for reproduction, learnable evolutionary evaluators for function evaluations, and learnable evolutionary transfer modules for sharing or reusing optimization experience). The insight of learnable MOEAs is offered to readers as a reference to the general track of the efforts in this field.

The use of visual sensors is flourishing, driven among others by the several applications in detection and prevention of crimes or dangerous events. While the problem of optimal camera placement for total coverage has been solved for a decade or so, that of the arrangement of cameras maximizing the recognition of objects "in-transit" is still open. The objective of this paper is to attack this problem by providing an adversarial method of proven optimality based on the resolution of Hamilton-Jacobi equations. The problem is attacked by first assuming the perspective of an adversary, i.e. computing explicitly the path minimizing the probability of detection and the quality of reconstruction. Building on this result, we introduce an optimality measure for camera configurations and perform a simulated annealing algorithm to find the optimal camera placement.

Purpose of this paper: This paper studies using a mobile manipulator with a collaborative robotic arm component to manipulate objects beyond the robot's maximum payload. Design/methodology/approach: The paper proposes a single-short probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method's help, we can plan contact-rich manipulation without particularly analyzing an object's contact modes and their transitions. The planner and optimizer determine them automatically. Findings: We conducted experiments and analyses using simulations and real-world executions to examine the method's performance. The method successfully found manipulation motion that met contact, force, and kinematic constraints. It allowed a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles. What is original/value of paper: The paper presents an automatic approach for solving contact-rich heavy object manipulation problems. Unlike previous methods, the new approach does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, nonprehensile manipulation, manipulation with contact, etc.

Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning. The primary concerns in FedOpt can be attributed to the model divergence and communication efficiency, which significantly affect the performance. In this paper, we propose a new method, i.e., LoSAC, to learn from heterogeneous distributed data more efficiently. Its key algorithmic insight is to locally update the estimate for the global full gradient after {each} regular local model update. Thus, LoSAC can keep clients' information refreshed in a more compact way. In particular, we have studied the convergence result for LoSAC. Besides, the bonus of LoSAC is the ability to defend the information leakage from the recent technique Deep Leakage Gradients (DLG). Finally, experiments have verified the superiority of LoSAC comparing with state-of-the-art FedOpt algorithms. Specifically, LoSAC significantly improves communication efficiency by more than $100\%$ on average, mitigates the model divergence problem and equips with the defense ability against DLG.

Federated learning enables isolated clients to train a shared model collaboratively by aggregating the locally-computed gradient updates. However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an honest-but-curious server. Although the additive homomorphic encryption technique guarantees the security of this process, it brings unacceptable computation and communication burdens to FL participants. To mitigate this cost of secure aggregation and maintain the learning performance, we propose a new framework called Encoded Gradient Aggregation (\emph{EGA}). In detail, EGA first encodes local gradient updates into an encoded domain with injected noises in each client before the aggregation in the server. Then, the encoded gradients aggregation results can be recovered for the global model update via a decoding function. This scheme could prevent the raw gradients of a single client from exposing on the internet and keep them unknown to the server. EGA could provide optimization and communication benefits under different noise levels and defend against gradient leakage. We further provide a theoretical analysis of the approximation error and its impacts on federated optimization. Moreover, EGA is compatible with the most federated optimization algorithms. We conduct intensive experiments to evaluate EGA in real-world federated settings, and the results have demonstrated its efficacy.

Autonomous vehicles face the problem of optimizing the expected performance of subsequent maneuvers while bounding the risk of collision with surrounding dynamic obstacles. These obstacles, such as agent vehicles, often exhibit stochastic transitions that should be accounted for in a timely and safe manner. The Constrained Stochastic Shortest Path problem (C-SSP) is a formalism for planning in stochastic environments under certain types of operating constraints. While C-SSP allows specifying constraints in the planning problem, it does not allow for bounding the probability of constraint violation, which is desired in safety-critical applications. This work's first contribution is an exact integer linear programming formulation for Chance-constrained SSP (CC-SSP) that attains deterministic policies. Second, a randomized rounding procedure is presented for stochastic policies. Third, we show that the CC-SSP formalism can be generalized to account for constraints that span through multiple time steps. Evaluation results show the usefulness of our approach in benchmark problems compared to existing approaches.

Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the-art classification accuracy is often achieved by ensemble learning where results are synthesized from multiple base models. This characteristic implies that ensemble learning needs substantial computing resources, preventing their use in resource-limited environments, such as in edge devices. To extend the applicability of ensemble learning, we propose the LightTS framework that compresses large ensembles into lightweight models while ensuring competitive accuracy. First, we propose adaptive ensemble distillation that assigns adaptive weights to different base models such that their varying classification capabilities contribute purposefully to the training of the lightweight model. Second, we propose means of identifying Pareto optimal settings w.r.t. model accuracy and model size, thus enabling users with a space budget to select the most accurate lightweight model. We report on experiments using 128 real-world time series sets and different types of base models that justify key decisions in the design of LightTS and provide evidence that LightTS is able to outperform competitors.

This paper introduces a general framework for iterative optimization algorithms and establishes under general assumptions that their convergence is asymptotically geometric. We also prove that under appropriate assumptions, the rate of convergence can be lower bounded. The convergence is then only geometric, and we provide the exact asymptotic convergence rate. This framework allows to deal with constrained optimization and encompasses the Expectation Maximization algorithm and the mirror descent algorithm, as well as some variants such as the alpha-Expectation Maximization or the Mirror Prox algorithm.Furthermore, we establish sufficient conditions for the convergence of the Mirror Prox algorithm, under which the method converges systematically to the unique minimizer of a convex function on a convex compact set.