A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
CoTj improves diffusion model quality and stability via graph planning.
This work tackles long-term visual planning by goal-conditioned hierarchical predictors.
Study shows bifurcation in optimal retirement planning.
We study control systems invariant under a Lie group with application to the problem of nonlinear trajectory planning. A theory of symmetry reduction of exterior differential systems is employed to demonstrate how symmetry reduction and reconstruction is effective in the explicit, exact construction of planned system t…
PBCS combines RL and motion planning for better exploration.
In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data. In this work, we ask how to imagine goal-directed visual plans -- a plausible sequence of observations that transition a dynamical system …
A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a for…
PlanGAN uses GANs to plan efficient trajectories for multi-goal tasks in sparse reward environments.
Intelligent motion planning is one of the core components in automated vehicles, which has received extensive interests. Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability. Sampling based methods cannot guarantee the optimality of the gener…
Myriad offers a testbed for integrating machine learning and trajectory optimization.
New method for predicting paths of unpredictable objects with high confidence.
Many AI problems, in robotics and other domains, are goal-directed, essentially seeking a trajectory leading to some goal state. In such problems, the way we choose to represent a trajectory underlies algorithms for trajectory prediction and optimization. Interestingly, most all prior work in imitation and reinforcemen…
In this work, we take a representation learning perspective on hierarchical reinforcement learning, where the problem of learning lower layers in a hierarchy is transformed into the problem of learning trajectory-level generative models. We show that we can learn continuous latent representations of trajectories, which…
Develops a method to plan exploration that learns strong policies with fewer samples.
GP-ND avoids obstacles in trajectory planning using Gaussian Process regression.
New method learns state embeddings from demonstrations for improved reinforcement learning.
One-shot path planning for multiple agents using neural networks.
Swept Volume (SV), the volume displaced by an object when it is moving along a trajectory, is considered a useful metric for motion planning. First, SV has been used to identify collisions along a trajectory, because it directly measures the amount of space required for an object to move. Second, in sampling-based moti…
This work enables UAVs to autonomously form desired trajectories without needing a central plan.
This work improves motion planning for quadcopters by learning and reasoning about controller performance.
Improved CEM for fast real-time planning in high-dimensional control tasks.
Deep network solves maze path planning without training.
End-to-end learnable network for safer self-driving with interpretable intermediate representations.
Model based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model based control algorithms to re-plan often, thus being computationally expensive, suboptimal and not reliable. In this work, we propose a model agnostic method for estimating the uncertainty of a model?s pr…
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting inaccuracies of the learned model. We propose to regularize trajectory optimization by means of a denoising autoencoder that is trained on the …
Active learning method estimates nonlinear systems efficiently.
This paper surveys DRL for autonomous vehicle motion planning.
We propose a plan online and learn offline (POLO) framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local traj…
Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly. Such prediction should be probabilistic, to address the uncertainties in human behavior. Such prediction should also be in…
Neural A* uses machine learning to improve path planning efficiency.
Paper uses deep imitation learning to predict aircraft trajectories accurately.
Method learns reusable actions from human demonstrations.
Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory fr…
This work provides safety guarantees for iterative GP predictions.
The paper proposes a model to forecast traffic motion from sensor data.
Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. Yet, reward functions that evoke desirable behavior are often difficul…
A new neural network captures and explains trajectory patterns.
A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real…
Accurately predicting future behaviors of surrounding vehicles is an essential capability for autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others, however, are full of uncertainties. Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of th…
CEM-GD combines CEM and gradient descent for efficient model-based RL.
New geometric approach controls motion of a spinning sphere on a plane.
We introduce a method for learning the dynamics of complex nonlinear systems based on deep generative models over temporal segments of states and actions. Unlike dynamics models that operate over individual discrete timesteps, we learn the distribution over future state trajectories conditioned on past state, past acti…
Models for predicting aircraft motion are an important component of modern aeronautical systems. These models help aircraft plan collision avoidance maneuvers and help conduct offline performance and safety analyses. In this article, we develop a method for learning a probabilistic generative model of aircraft motion i…
This work improves AI's ability to solve physical tasks by optimizing world models in abstracted spaces.
New algorithm reduces sample complexity for planning in MDPs.
Vanguard uses AI to create personalized financial plans.
This research enhances exploration in DDPG using latent trajectory optimization.
Proposes a CNN-based method for better trajectory owner prediction.