Constrained Stein Variational Trajectory Optimization

• URL: http://arxiv.org/abs/2308.12110v3
• Date: Tue, 23 Jul 2024 08:52:31 GMT
• Title: Constrained Stein Variational Trajectory Optimization
• Authors: Thomas Power, Dmitry Berenson,
• Abstract summary: We present CSVTO, an algorithm for performing trajectory optimization with constraints on a set of trajectories in parallel. By explicitly generating diverse sets of trajectories, CSVTO is better able to avoid poor local minima. We demonstrate that CSVTO outperforms baselines in challenging highly-constrained tasks.
• Score: 5.317624228510749
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Constrained Stein Variational Trajectory Optimization (CSVTO), an algorithm for performing trajectory optimization with constraints on a set of trajectories in parallel. We frame constrained trajectory optimization as a novel form of constrained functional minimization over trajectory distributions, which avoids treating the constraints as a penalty in the objective and allows us to generate diverse sets of constraint-satisfying trajectories. Our method uses Stein Variational Gradient Descent (SVGD) to find a set of particles that approximates a distribution over low-cost trajectories while obeying constraints. CSVTO is applicable to problems with differentiable equality and inequality constraints and includes a novel particle re-sampling step to escape local minima. By explicitly generating diverse sets of trajectories, CSVTO is better able to avoid poor local minima and is more robust to initialization. We demonstrate that CSVTO outperforms baselines in challenging highly-constrained tasks, such as a 7DoF wrench manipulation task, where CSVTO outperforms all baselines both in success and constraint satisfaction.

Related papers

• Path Signatures for Diversity in Probabilistic Trajectory Optimisation [24.101232487591094]
  Motion planning can be cast as a trajectory optimisation problem where a cost is minimised as a function of the trajectory being generated. Recent advancements in computing hardware allow for parallel trajectory optimisation where multiple solutions are obtained simultaneously. We propose an algorithm for parallel trajectory optimisation that promotes diversity over the range of solutions, therefore avoiding mode collapses.
  arXiv  Detail & Related papers  (2023-08-08T06:10:53Z)
• Independent Component Alignment for Multi-Task Learning [2.5234156040689237]
  In a multi-task learning (MTL) setting, a single model is trained to tackle a diverse set of tasks jointly. We propose using a condition number of a linear system of gradients as a stability criterion of an MTL optimization. We present Aligned-MTL, a novel MTL optimization approach based on the proposed criterion.
  arXiv  Detail & Related papers  (2023-05-30T12:56:36Z)
• DDPEN: Trajectory Optimisation With Sub Goal Generation Model [70.36888514074022]
  In this paper, we produce a novel Differential Dynamic Programming with Escape Network (DDPEN) We propose to utilize a deep model that takes as an input map of the environment in the form of a costmap together with the desired position. The model produces possible future directions that will lead to the goal, avoiding local minima which is possible to run in real time conditions.
  arXiv  Detail & Related papers  (2023-01-18T11:02:06Z)
• Robust Constrained Multi-objective Evolutionary Algorithm based on Polynomial Chaos Expansion for Trajectory Optimization [0.0]
  The proposed method rewrites a robust formulation into a deterministic problem via the PCE. As a case study, the landing trajectory design of supersonic transport (SST) with wind uncertainty is optimized.
  arXiv  Detail & Related papers  (2022-05-23T15:33:05Z)
• Online Learning to Transport via the Minimal Selection Principle [2.3857747529378917]
  We study the Online Learning Transport (OLT) problem where the decision variable is a convex, an-dimensional object. We derive a novel method called the minimal selection or exploration (SoMLT) algorithm to solve OLT problems using mean-field and discretization techniques.
  arXiv  Detail & Related papers  (2022-02-09T21:25:58Z)
• On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems [99.59934203759754]
  We introduce a class of first-order methods for smooth constrained optimization. Two distinctive features of our approach are that projections or optimizations over the entire feasible set are avoided. The resulting algorithmic procedure is simple to implement even when constraints are nonlinear.
  arXiv  Detail & Related papers  (2021-07-17T11:45:13Z)
• Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks [59.419152768018506]
  We show that any optimal policy necessarily satisfies the k-SP constraint. We propose a novel cost function that penalizes the policy violating SP constraint, instead of completely excluding it. Our experiments on MiniGrid, DeepMind Lab, Atari, and Fetch show that the proposed method significantly improves proximal policy optimization (PPO)
  arXiv  Detail & Related papers  (2021-07-13T21:39:21Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Potential Function-based Framework for Making the Gradients Small in Convex and Min-Max Optimization [14.848525762485872]
  Making the gradients small is a fundamental optimization problem that has eluded unifying and simple convergence arguments. We introduce a novel potential function-based framework to study the convergence of standard methods for making the gradients small.
  arXiv  Detail & Related papers  (2021-01-28T16:41:00Z)
• On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization [55.937424268654645]
  We consider algorithm-independent lower bounds for the problem of black-box optimization of functions having a bounded norm. We provide a novel proof technique for deriving lower bounds on the regret, with benefits including simplicity, versatility, and an improved dependence on the error probability.
  arXiv  Detail & Related papers  (2020-08-20T03:48:14Z)
• Conditional gradient methods for stochastically constrained convex minimization [54.53786593679331]
  We propose two novel conditional gradient-based methods for solving structured convex optimization problems. The most important feature of our framework is that only a subset of the constraints is processed at each iteration. Our algorithms rely on variance reduction and smoothing used in conjunction with conditional gradient steps, and are accompanied by rigorous convergence guarantees.
  arXiv  Detail & Related papers  (2020-07-07T21:26:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.