Multi-Objective Trajectory Planning with Dual-Encoder
- URL: http://arxiv.org/abs/2403.17353v1
- Date: Tue, 26 Mar 2024 03:32:45 GMT
- Title: Multi-Objective Trajectory Planning with Dual-Encoder
- Abstract summary: Time-jerk optimal trajectory planning is crucial in advancing robotic arms' performance in dynamic tasks.
Traditional methods rely on solving complex nonlinear programming problems.
We propose a two-stage approach to accelerate time-jerk optimal trajectory planning.
- Score: 8.414908539114743
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Time-jerk optimal trajectory planning is crucial in advancing robotic arms' performance in dynamic tasks. Traditional methods rely on solving complex nonlinear programming problems, bringing significant delays in generating optimized trajectories. In this paper, we propose a two-stage approach to accelerate time-jerk optimal trajectory planning. Firstly, we introduce a dual-encoder based transformer model to establish a good preliminary trajectory. This trajectory is subsequently refined through sequential quadratic programming to improve its optimality and robustness. Our approach outperforms the state-of-the-art by up to 79.72\% in reducing trajectory planning time. Compared with existing methods, our method shrinks the optimality gap with the objective function value decreasing by up to 29.9\%.
Related papers
- Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions [1.3299507495084417]
This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) handover missions.<n>We propose a neural surrogate planner utilizing decoupled encoder-decoder long short-term memory (LSTM) networks to generate coordinated handover trajectory predictions.<n> Benchmark evaluations demonstrate that the learning-augmented planning framework achieves more than a threefold speedup and 100% optimization success rate.
arXiv Detail & Related papers (2026-05-19T09:07:59Z) - TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment [52.570581883709345]
We propose Trajectory Matching Policy Balance Optimization (TMPO), which replaces reward with human-level reward distribution matching.<n>TMPO improves generative diversity over state-of-the-art methods by 9.1%, and achieves competitive performance in all downstream and efficiency metrics.<n>To further reduce multi-trajectory training time on large-scale flow- prefixes, TMPO incorporates Dynamic Tree Sampling models, where trajectories share denoising and branch at dynamically scheduled steps.
arXiv Detail & Related papers (2026-05-09T04:41:02Z) - HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving [51.268878540511054]
We propose HAD, an end-to-end planning framework with a Hierarchical Diffusion Policy.<n>We show that HAD achieves new state-of-the-art performance on both NAVSIM and HUGSIM.
arXiv Detail & Related papers (2026-04-04T04:12:47Z) - Diff3R: Feed-forward 3D Gaussian Splatting with Uncertainty-aware Differentiable Optimization [76.38917994186733]
Diff3R is a novel framework that bridges feed-forward prediction and test-time optimization.<n>We show that it can be seamlessly integrated into existing feed-forward 3DGS architectures for both pose-given and pose-free methods.
arXiv Detail & Related papers (2026-04-01T15:40:20Z) - FlowConsist: Make Your Flow Consistent with Real Trajectory [99.22869983378062]
We argue that current fast-flow training paradigms suffer from two fundamental issues.<n> conditional velocities constructed from randomly paired noise-data samples introduce systematic trajectory drift.<n>We propose FlowConsist, a training framework designed to enforce trajectory consistency in fast flows.
arXiv Detail & Related papers (2026-02-06T03:24:23Z) - Learning-based Initialization of Trajectory Optimization for Path-following Problems of Redundant Manipulators [22.18634506601658]
Trajectory optimization is an efficient tool to generate a redundant manipulator's joint trajectory following a 6-dimensional path.<n>We present a learning-based initial trajectory generation method that generates high-quality initial trajectories in a short time budget.
arXiv Detail & Related papers (2026-02-03T11:44:20Z) - Heteroscedastic Bayesian Optimization-Based Dynamic PID Tuning for Accurate and Robust UAV Trajectory Tracking [12.655695094805159]
HBO-PID is a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization framework with the classical controller.<n>It improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.
arXiv Detail & Related papers (2025-12-30T14:21:17Z) - Closing the Train-Test Gap in World Models for Gradient-Based Planning [64.36544881136405]
We propose improved methods for training world models that enable efficient gradient-based planning.<n>At test time, our approach outperforms or matches the classical gradient-free cross-entropy method.
arXiv Detail & Related papers (2025-12-10T18:59:45Z) - Optimizing Optimizers for Fast Gradient-Based Learning [53.81268610971847]
We lay the theoretical foundation for automating design in gradient-based learning.<n>By treating gradient loss signals as a function that translates to parameter motions, the problem reduces to a family of convex optimization problems.
arXiv Detail & Related papers (2025-12-06T09:50:41Z) - DELTAv2: Accelerating Dense 3D Tracking [79.63990337419514]
We propose a novel algorithm for accelerating dense long-term 3D point tracking in videos.<n>We introduce a coarse-to-fine strategy that begins tracking with a small subset of points and progressively expands the set of tracked trajectories.<n>The newly added trajectories are using a learnable module, which is trained end-to-end alongside the tracking network.
arXiv Detail & Related papers (2025-08-02T03:15:47Z) - Optimizing ML Training with Metagradient Descent [69.89631748402377]
We introduce an algorithm for efficiently calculating metagradients -- gradients through model training -- at scale.
We then introduce a "smooth model training" framework that enables effective optimization using metagradients.
arXiv Detail & Related papers (2025-03-17T22:18:24Z) - World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning [60.100794160682646]
We propose a new learning framework that jointly optimize state prediction and action selection through preference learning.
To automatically collect trajectories and stepwise preference data without human annotation, we introduce a tree search mechanism for extensive exploration via trial-and-error.
Our method significantly outperforms existing methods and GPT-4o when applied to Qwen2-VL (7B), LLaVA-1.6 (7B), and LLaMA-3.2 (11B)
arXiv Detail & Related papers (2025-03-13T15:49:56Z) - Towards Robust Spacecraft Trajectory Optimization via Transformers [17.073280827888226]
Future multi-spacecraft missions require robust autonomous optimization capabilities to ensure safe and efficient rendezvous operations.
To mitigate this burden, introduced generative Transformer model to provide robust optimal initial guesses.
This work extends capabilities of ART to address robustconstrained optimal control problems.
arXiv Detail & Related papers (2024-10-08T00:58:42Z) - Shadow Program Inversion with Differentiable Planning: A Framework for Unified Robot Program Parameter and Trajectory Optimization [6.890628942323211]
SPI-DP is a novel first-order optimization approach for robot programs.
We introduce DGPMP2-ND, a collision-free motion planner for serial N-DoF kinematics.
We provide a comprehensive evaluation on two practical household and industrial applications.
arXiv Detail & Related papers (2024-09-13T09:46:41Z) - Diffusion Models as Optimizers for Efficient Planning in Offline RL [47.0835433289033]
Diffusion models have shown strong competitiveness in offline reinforcement learning tasks.
We propose a faster autoregressive model to handle the generation of feasible trajectories.
This allows us to achieve more efficient planning without sacrificing capability.
arXiv Detail & Related papers (2024-07-23T03:00:01Z) - Trajectory-Based Multi-Objective Hyperparameter Optimization for Model Retraining [8.598456741786801]
We present a novel trajectory-based multi-objective Bayesian optimization algorithm.
Our algorithm outperforms the state-of-the-art multi-objectives in both locating better trade-offs and tuning efficiency.
arXiv Detail & Related papers (2024-05-24T07:43:45Z) - An Efficient Learning-based Solver Comparable to Metaheuristics for the
Capacitated Arc Routing Problem [67.92544792239086]
We introduce an NN-based solver to significantly narrow the gap with advanced metaheuristics.
First, we propose direction-aware facilitating attention model (DaAM) to incorporate directionality into the embedding process.
Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy.
arXiv Detail & Related papers (2024-03-11T02:17:42Z) - Locally Optimal Descent for Dynamic Stepsize Scheduling [45.6809308002043]
We introduce a novel dynamic learning scheduling scheme grounded in theory with the goal of simplifying the manual and time-consuming tuning of schedules in phases.
Our approach is based on estimating a locally-optimal practice tuning-rate in the direction of a smooth gradient.
Our findings indicate that our method needs minimal tuning when compared to existing approaches.
arXiv Detail & Related papers (2023-11-23T09:57:35Z) - Accelerating Cutting-Plane Algorithms via Reinforcement Learning
Surrogates [49.84541884653309]
A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms.
Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability.
We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
arXiv Detail & Related papers (2023-07-17T20:11:56Z) - Time-Optimal Planning for Quadrotor Waypoint Flight [50.016821506107455]
Planning time-optimal trajectories at the actuation limit of a quadrotor is an open problem.
We propose a solution while exploiting the full quadrotor's actuator potential.
We validate our method in real-world flights in one of the world's largest motion-capture systems.
arXiv Detail & Related papers (2021-08-10T09:26:43Z) - Autonomous Drone Racing with Deep Reinforcement Learning [39.757652701917166]
In many robotic tasks, such as drone racing, the goal is to travel through a set of waypoints as fast as possible.
A key challenge is planning the minimum-time trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance.
In this work, a new approach to minimum-time trajectory generation for quadrotors is presented.
arXiv Detail & Related papers (2021-03-15T18:05:49Z) - Combining Deep Learning and Optimization for Security-Constrained
Optimal Power Flow [94.24763814458686]
Security-constrained optimal power flow (SCOPF) is fundamental in power systems.
Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs.
This paper proposes a novel approach that combines deep learning and robust optimization techniques.
arXiv Detail & Related papers (2020-07-14T12:38:21Z) - Adversarial Generation of Informative Trajectories for Dynamics System
Identification [3.664687661363732]
We show how to generate excitation trajectories that are diverse in both control parameter and inertial parameter spaces.
This is the first robotics work to explore system identification with multiple cyclic trajectories.
We also show how to scale this approach even further by increasing the generation speed and quality of the dataset.
arXiv Detail & Related papers (2020-03-02T21:09:18Z)
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.