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Motion Planning

3260 papers • 126 benchmarks • 313 datasets

( Image credit: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in motion-planning-7

Trend

Dataset

Best Model

Actions

nuScenes

Libraries

i

Use these libraries to find motion-planning-7 models and implementations

caelan/pddlstream

3 papers 329

Datasets

nuScenes

HouseExpo

Motion Policy Networks

ThreeDWorld Transport Challenge

PushWorld

Subtasks

No subtasks available.

Most implemented papers

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Stefan Milz, Martin Simon, Karl Amende, H. Groß•Thu Mar 15 2018

Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only, is introduced and a specific Euler-Region-Proposal Network (E-RPN) is proposed to estimate the pose of the object by adding an imaginary and a real fraction to the regression network.

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Paper Graph

Learning Latent Dynamics for Planning from Pixels

T. Lillicrap, Honglak Lee, Danijar Hafner, David R Ha, Ruben Villegas, James Davidson, Ian S. Fischer•Sun Nov 11 2018

The Deep Planning Network (PlaNet) is proposed, a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space using a latent dynamics model with both deterministic and stochastic transition components.

1685 0

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Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

J. How, Michael Everett, Yu Fan Chen•Thu May 03 2018

This work extends the previous approach to develop an algorithm that learns collision avoidance among a variety of types of dynamic agents without assuming they follow any particular behavior rules, and introduces a strategy using LSTM that enables the algorithm to use observations of an arbitrary number of other agents, instead of previous methods that have a fixed observation size.

583 0

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STRIPS Planning in Infinite Domains

L. Kaelbling, Tomas Lozano-Perez, Caelan Reed Garrett•Sat Dec 31 2016

STRIPStream is introduced: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints by reducing STRIPStream problems to a sequence of finite-domain planning problems.

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PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning

L. Kaelbling, Tomas Lozano-Perez, Caelan Reed Garrett•Thu Feb 22 2018

This work provides domain-independent algorithms that reduce PDDLStream problems to a sequence of finite PDDL problems and introduces an algorithm that dynamically balances exploring new candidate plans and exploiting existing ones to solve tightly-constrained problems.

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Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach

Hiroyasu Tsukamoto, Soon-Jo Chung•Sun Jun 07 2020

This letter presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric, and demonstrates how to exploit NCMs to design an online optimal estimator and controller for nonlinear systems with bounded disturbances utilizing their duality.

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One Thousand and One Hours: Self-driving Motion Prediction Dataset

Ashesh Jain, V. Iglovikov, Sammy Omari, G. Zuidhof, J. Houston, Luca Bergamini, Yawei Ye, Peter Ondruska•Wed Jun 24 2020

This collection was collected by a fleet of 20 autonomous vehicles along a fixed route in Palo Alto, California over a four-month period and forms the largest, most complete and detailed dataset to date for the development of self-driving, machine learning tasks such as motion forecasting, planning and simulation.

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EagerMOT: 3D Multi-Object Tracking via Sensor Fusion

L. Leal-Taixé, Aljosa Osep, Aleksandr Kim•Wed Apr 28 2021

This paper proposes EagerMOT, a simple tracking formulation that eagerly integrates all available object observations from both sensor modalities to obtain a well-informed interpretation of the scene dynamics and achieves state-of-the-art results across several MOT tasks on the KITTI and NuScenes datasets.

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Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

Rui Wang, Kostas E. Bekris, Yinglong Miao•Tue Oct 05 2021

This work proposes a new efficient and complete solver under general constraints for monotone instances, which can be solved by moving each object at most once, which results in 57.3% faster computation and 3 times higher success rate than state-of-the-art methods.

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