{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/motion-planning/papers/8","list_of":"/task/motion-planning","task":"Motion Planning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":8,"pages_in_order":10,"rows_per_page":100,"rows":[701,800],"of":920,"counts":{"archive_papers_tagged":920,"with_a_code_link":252,"where_syntology_ran_a_sample":59,"not_listed_spam_title":0,"listed":920,"listed_where_code_ran":59,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":50,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":50,"listed_every_run_a_failure_of_syntologys_instrument":9,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/motion-planning","prev":"/task/motion-planning/papers/7","next":"/task/motion-planning/papers/9","papers":[{"url":null,"slug":"nebula-quest-for-robotic-autonomy-in","title":"NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge","date":"2021-03-21","arxiv_id":"2103.11470","repositories_listed":0,"syntology":null},{"url":null,"slug":"ia-planner-motion-planning-using","title":"IA Planner: Motion Planning Using Instantaneous Analysis for Autonomous Vehicle in the Dense Dynamic Scenarios on Highways","date":"2021-03-19","arxiv_id":"2103.10909","repositories_listed":0,"syntology":null},{"url":null,"slug":"hands-a-multimodal-dataset-for-modeling","title":"HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands","date":"2021-03-08","arxiv_id":"2103.04845","repositories_listed":0,"syntology":null},{"url":null,"slug":"show-me-what-you-can-do-capability","title":"Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration","date":"2021-03-06","arxiv_id":"2103.04077","repositories_listed":0,"syntology":null},{"url":null,"slug":"step-stochastic-traversability-evaluation-and","title":"STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation","date":"2021-03-04","arxiv_id":"2103.02828","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-simultaneous-multi-step","title":"Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map","date":"2021-03-01","arxiv_id":"2103.01039","repositories_listed":0,"syntology":null},{"url":null,"slug":"imitation-learning-for-robust-and-safe-real","title":"Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach","date":"2021-02-25","arxiv_id":"2102.12668","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-planning-for-a-pair-of-tethered-robots","title":"Motion Planning for a Pair of Tethered Robots","date":"2021-02-25","arxiv_id":"2102.13212","repositories_listed":0,"syntology":null},{"url":null,"slug":"scout-socially-consistent-and-understandable","title":"SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs","date":"2021-02-12","arxiv_id":"2102.06361","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-distributed-collaborative","title":"Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients","date":"2021-02-11","arxiv_id":"2102.06284","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-interaction-aware-trajectory","title":"Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments","date":"2021-02-10","arxiv_id":"2102.05382","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-advantage-actor-critic-algorithm-for","title":"An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios","date":"2021-02-05","arxiv_id":"2102.03138","repositories_listed":0,"syntology":null},{"url":null,"slug":"experience-based-heuristic-search-robust","title":"Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning","date":"2021-02-05","arxiv_id":"2102.03127","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-motion-planning-algorithms-for","title":"A review of motion planning algorithms for intelligent robotics","date":"2021-02-04","arxiv_id":"2102.02376","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-integrated-localisation-motion-planning","title":"An Integrated Localisation, Motion Planning and Obstacle Avoidance Algorithm in Belief Space","date":"2021-01-27","arxiv_id":"2101.11566","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-based-temporal-logic-1","title":"Reinforcement Learning Based Temporal Logic Control with Soft Constraints Using Limit-deterministic Generalized Buchi Automata","date":"2021-01-25","arxiv_id":"2101.10284","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-multi-task-learning-for-joint","title":"Deep Multi-Task Learning for Joint Localization, Perception, and Prediction","date":"2021-01-17","arxiv_id":"2101.06720","repositories_listed":0,"syntology":null},{"url":null,"slug":"bootstrapping-motor-skill-learning-with","title":"Bootstrapping Motor Skill Learning with Motion Planning","date":"2021-01-12","arxiv_id":"2101.04736","repositories_listed":0,"syntology":null},{"url":null,"slug":"qrrt-quality-biased-incremental-rrt-for","title":"qRRT: Quality-Biased Incremental RRT for Optimal Motion Planning in Non-Holonomic Systems","date":"2021-01-07","arxiv_id":"2101.02635","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-reinforcement-learning-for-autonomous","title":"Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning","date":"2021-01-01","arxiv_id":"2101.00186","repositories_listed":0,"syntology":null},{"url":"/paper/large-scale-interactive-motion-forecasting-1","slug":"large-scale-interactive-motion-forecasting-1","title":"Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-adaptive-control-for-uncertain","title":"Universal Adaptive Control of Nonlinear Systems","date":"2020-12-31","arxiv_id":"2012.15815","repositories_listed":0,"syntology":null},{"url":null,"slug":"forming-human-robot-cooperation-for-tasks","title":"Forming Real-World Human-Robot Cooperation for Tasks With General Goal","date":"2020-12-19","arxiv_id":"2012.10773","repositories_listed":0,"syntology":null},{"url":null,"slug":"pepscenes-a-novel-dataset-and-baseline-for","title":"PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D","date":"2020-12-14","arxiv_id":"2012.07773","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-to-go-function-generating-networks-for","title":"Cost-to-Go Function Generating Networks for High Dimensional Motion Planning","date":"2020-12-10","arxiv_id":"2012.06023","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-swarm2-planning-and-control-of","title":"Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions","date":"2020-12-10","arxiv_id":"2012.05457","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-reactive-probabilistic-motion-planning","title":"Fast-reactive probabilistic motion planning for high-dimensional robots","date":"2020-12-03","arxiv_id":"2012.02118","repositories_listed":0,"syntology":null},{"url":null,"slug":"coinbot-intelligent-robotic-coin-bag","title":"Coinbot: Intelligent Robotic Coin Bag Manipulation Using Deep Reinforcement Learning And Machine Teaching","date":"2020-12-02","arxiv_id":"2012.01356","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-find-shortest-collision-free","title":"Unsupervised Path Regression Networks","date":"2020-11-30","arxiv_id":"2011.14787","repositories_listed":0,"syntology":null},{"url":null,"slug":"relation3dmot-exploiting-deep-affinity-for-3d","title":"Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation","date":"2020-11-25","arxiv_id":"2011.12850","repositories_listed":0,"syntology":null},{"url":null,"slug":"risk-sensitive-motion-planning-using-entropic","title":"Risk-Sensitive Motion Planning using Entropic Value-at-Risk","date":"2020-11-23","arxiv_id":"2011.11211","repositories_listed":0,"syntology":null},{"url":null,"slug":"reactive-human-to-robot-handovers-of","title":"Reactive Human-to-Robot Handovers of Arbitrary Objects","date":"2020-11-17","arxiv_id":"2011.08961","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-long-horizon-planning-framework-for","title":"A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects","date":"2020-11-16","arxiv_id":"2011.08177","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-human-robot-coexistence-space","title":"Designing Human-Robot Coexistence Space","date":"2020-11-14","arxiv_id":"2011.07374","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-aware-constraint-learning-for","title":"Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations","date":"2020-11-09","arxiv_id":"2011.04141","repositories_listed":0,"syntology":null},{"url":null,"slug":"reactive-motion-planning-with-probabilistics","title":"Reactive motion planning with probabilistic safety guarantees","date":"2020-11-06","arxiv_id":"2011.03590","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-relevant-representation-learning-for","title":"Task-relevant Representation Learning for Networked Robotic Perception","date":"2020-11-06","arxiv_id":"2011.03216","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrating-deep-learning-based-image","title":"Deep-Learning-Aided Path Planning and Map Construction for Expediting Indoor Mapping","date":"2020-11-03","arxiv_id":"2011.02043","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceive-attend-and-drive-learning-spatial","title":"Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving","date":"2020-11-02","arxiv_id":"2011.01153","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesis-of-discounted-reward-optimal","title":"Synthesis of Discounted-Reward Optimal Policies for Markov Decision Processes Under Linear Temporal Logic Specifications","date":"2020-11-01","arxiv_id":"2011.00632","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-plan-optimally-with-flow-based","title":"Learning to Plan Optimally with Flow-based Motion Planner","date":"2020-10-21","arxiv_id":"2010.11323","repositories_listed":0,"syntology":null},{"url":null,"slug":"planning-with-learned-dynamics-guaranteed","title":"Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants","date":"2020-10-18","arxiv_id":"2010.08993","repositories_listed":0,"syntology":null},{"url":null,"slug":"constrained-motion-planning-networks-x","title":"Constrained Motion Planning Networks X","date":"2020-10-17","arxiv_id":"2010.08707","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-motion-planning-using-deep","title":"Multi-Agent Motion Planning using Deep Learning for Space Applications","date":"2020-10-15","arxiv_id":"2010.07935","repositories_listed":0,"syntology":null},{"url":null,"slug":"broadly-exploring-local-policy-trees-for-long","title":"Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning","date":"2020-10-13","arxiv_id":"2010.06491","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrated-task-and-motion-planning","title":"Integrated Task and Motion Planning","date":"2020-10-02","arxiv_id":"2010.01083","repositories_listed":0,"syntology":null},{"url":null,"slug":"madras-multi-agent-driving-simulator","title":"MADRaS : Multi Agent Driving Simulator","date":"2020-10-02","arxiv_id":"2010.00993","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-multi-robot-task-motion-planning-for","title":"Towards Multi-Robot Task-Motion Planning for Navigation in Belief Space","date":"2020-10-01","arxiv_id":"2010.00780","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-planning-combines-psychological-safety","title":"Motion Planning Combines Psychological Safety and Motion Prediction for a Sense Motive Robot","date":"2020-09-29","arxiv_id":"2010.11671","repositories_listed":0,"syntology":null},{"url":null,"slug":"with-whom-to-communicate-learning-efficient","title":"With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance","date":"2020-09-25","arxiv_id":"2009.12106","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-planning-by-reinforcement-learning-for","title":"Motion Planning by Reinforcement Learning for an Unmanned Aerial Vehicle in Virtual Open Space with Static Obstacles","date":"2020-09-24","arxiv_id":"2009.11799","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-finite-state-controllers-for-uncertain","title":"Robust Finite-State Controllers for Uncertain POMDPs","date":"2020-09-24","arxiv_id":"2009.11459","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-lane-detection-and-motion-planning","title":"Real-time Lane detection and Motion Planning in Raspberry Pi and Arduino for an Autonomous Vehicle Prototype","date":"2020-09-20","arxiv_id":"2009.09391","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-model-predictive-control-with-a","title":"Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version","date":"2020-09-20","arxiv_id":"2009.09381","repositories_listed":0,"syntology":null},{"url":null,"slug":"pomp-pomcp-based-online-motion-planning-for","title":"POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments","date":"2020-09-17","arxiv_id":"2009.08140","repositories_listed":0,"syntology":null},{"url":null,"slug":"path-planning-followed-by-kinodynamic","title":"Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)","date":"2020-08-29","arxiv_id":"2008.12950","repositories_listed":0,"syntology":null},{"url":null,"slug":"t-star-lite-a-fast-time-risk-optimal-motion","title":"T$^{\\star}$-Lite: A Fast Time-Risk Optimal Motion Planning Algorithm for Multi-Speed Autonomous Vehicles","date":"2020-08-29","arxiv_id":"2008.13048","repositories_listed":0,"syntology":null},{"url":null,"slug":"designing-inverse-dynamic-controller-with","title":"Designing inverse dynamic controller with integral action for motion planning of surgical robot in the presence of bounded disturbances","date":"2020-08-15","arxiv_id":"2008.08456","repositories_listed":0,"syntology":null},{"url":null,"slug":"dsdnet-deep-structured-self-driving-network","title":"DSDNet: Deep Structured self-Driving Network","date":"2020-08-13","arxiv_id":"2008.06041","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceive-predict-and-plan-safe-motion","title":"Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations","date":"2020-08-13","arxiv_id":"2008.05930","repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-the-safety-of-self-driving-vehicles","title":"Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction","date":"2020-08-13","arxiv_id":"2008.06020","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-manipulation-planning-on-constraint","title":"Neural Manipulation Planning on Constraint Manifolds","date":"2020-08-09","arxiv_id":"2008.03787","repositories_listed":0,"syntology":null},{"url":null,"slug":"near-optimal-reactive-synthesis-incorporating","title":"Near-Optimal Reactive Synthesis Incorporating Runtime Information","date":"2020-07-31","arxiv_id":"2007.16107","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-the-solution-manifold-in","title":"Learning the Solution Manifold in Optimization and Its Application in Motion Planning","date":"2020-07-24","arxiv_id":"2007.12397","repositories_listed":0,"syntology":null},{"url":null,"slug":"implicit-latent-variable-model-for-scene","title":"Implicit Latent Variable Model for Scene-Consistent Motion Forecasting","date":"2020-07-23","arxiv_id":"2007.12036","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomy-and-unmanned-vehicles-augmented","title":"Autonomy and Unmanned Vehicles Augmented Reactive Mission-Motion Planning Architecture for Autonomous Vehicles","date":"2020-07-19","arxiv_id":"2007.09563","repositories_listed":0,"syntology":null},{"url":null,"slug":"vrunet-multi-task-learning-model-for-intent","title":"VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users","date":"2020-07-10","arxiv_id":"2007.05397","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-sample-collision-with-neural","title":"Predicting Sample Collision with Neural Networks","date":"2020-06-30","arxiv_id":"2006.16868","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-rich-grid-mapping","title":"Confidence-rich grid mapping","date":"2020-06-29","arxiv_id":"2006.15754","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-motion-planning","title":"Graph Neural Networks for Motion Planning","date":"2020-06-11","arxiv_id":"2006.06248","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-visual-reasoning-learning-to-predict","title":"Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image","date":"2020-06-09","arxiv_id":"2006.05398","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-navigation-costs-from-demonstration-1","title":"Learning Navigation Costs from Demonstration with Semantic Observations","date":"2020-06-09","arxiv_id":"2006.05043","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-navigation-costs-from-demonstrations","title":"Learning Navigation Costs from Demonstrations with Semantic Observations","date":"2020-06-08","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-importance-of-prior-knowledge-in-precise","title":"The Importance of Prior Knowledge in Precise Multimodal Prediction","date":"2020-06-04","arxiv_id":"2006.02636","repositories_listed":0,"syntology":null},{"url":null,"slug":"n-2-c-neural-network-controller-design-using","title":"N 2 C : Neural Network Controller Design Using Behavioral Cloning","date":"2020-06-01","arxiv_id":"2006.00820","repositories_listed":0,"syntology":null},{"url":null,"slug":"combinatorics-of-a-discrete-trajectory-space","title":"Combinatorics of a Discrete Trajectory Space for Robot Motion Planning","date":"2020-05-25","arxiv_id":"2005.12064","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-naturalistic-driving-data-for","title":"Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving","date":"2020-05-23","arxiv_id":"2005.11470","repositories_listed":0,"syntology":null},{"url":null,"slug":"state-switching-control-of-the-second-order","title":"State-switching control of the second-order chained form system","date":"2020-05-22","arxiv_id":"2005.11114","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-lattice-based-motion-planning-with","title":"Enhancing Lattice-based Motion Planning with Introspective Learning and Reasoning","date":"2020-05-15","arxiv_id":"2005.07385","repositories_listed":0,"syntology":null},{"url":null,"slug":"overlapping-schwarz-decomposition-for","title":"On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control","date":"2020-05-14","arxiv_id":"2005.06674","repositories_listed":0,"syntology":null},{"url":null,"slug":"chance-constrained-trajectory-optimization","title":"Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems","date":"2020-05-09","arxiv_id":"2005.04374","repositories_listed":0,"syntology":null},{"url":null,"slug":"stereo-vision-for-unmanned-aerial","title":"Stereo Vision for Unmanned Aerial VehicleDetection, Tracking, and Motion Control","date":"2020-05-07","arxiv_id":"2005.04183","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-mapping-and-motion-planning-under","title":"Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments","date":"2020-04-26","arxiv_id":"2004.12317","repositories_listed":0,"syntology":null},{"url":null,"slug":"pbcs-efficient-exploration-and-exploitation","title":"PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning","date":"2020-04-24","arxiv_id":"2004.11667","repositories_listed":0,"syntology":null},{"url":null,"slug":"flexible-and-efficient-long-range-planning-1","title":"Flexible and Efficient Long-Range Planning Through Curious Exploration","date":"2020-04-22","arxiv_id":"2004.10876","repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-task-planning-and-situation","title":"Autonomous task planning and situation awareness in robotic surgery","date":"2020-04-19","arxiv_id":"2004.08911","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-resolution-a","title":"Multi-Resolution A*","date":"2020-04-14","arxiv_id":"2004.06684","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnn-encoder-to-reduce-the-dimensionality-of","title":"CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning","date":"2020-04-10","arxiv_id":"2004.05077","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-motion-planning-with-temporal","title":"Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks","date":"2020-04-02","arxiv_id":"2004.02610","repositories_listed":0,"syntology":null},{"url":null,"slug":"go-fetch-mobile-manipulation-in-unstructured","title":"Go Fetch: Mobile Manipulation in Unstructured Environments","date":"2020-04-02","arxiv_id":"2004.00899","repositories_listed":0,"syntology":null},{"url":null,"slug":"moment-state-dynamical-systems-for-nonlinear","title":"Moment State Dynamical Systems for Nonlinear Chance-Constrained Motion Planning","date":"2020-03-23","arxiv_id":"2003.10379","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-prediction-of-pedestrian","title":"Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions","date":"2020-03-22","arxiv_id":"2003.09996","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-reinforcement-learning-under","title":"Transfer Reinforcement Learning under Unobserved Contextual Information","date":"2020-03-09","arxiv_id":"2003.04427","repositories_listed":0,"syntology":null},{"url":null,"slug":"piecewise-linear-regressions-for","title":"Piecewise linear regressions for approximating distance metrics","date":"2020-02-27","arxiv_id":"2002.12466","repositories_listed":0,"syntology":null},{"url":null,"slug":"sub-goal-trees-a-framework-for-goal-based","title":"Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning","date":"2020-02-27","arxiv_id":"2002.12361","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-navigation-costs-from-demonstration","title":"Learning Navigation Costs from Demonstration in Partially Observable Environments","date":"2020-02-26","arxiv_id":"2002.11637","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-of-deep-reinforcement-learning-for","title":"Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles","date":"2020-01-30","arxiv_id":"2001.11231","repositories_listed":0,"syntology":null},{"url":null,"slug":"socially-intelligent-task-and-motion-planning","title":"Socially intelligent task and motion planning for human-robot interaction","date":"2020-01-23","arxiv_id":"2001.08398","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-motion-planning-for-dense-and","title":"Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning","date":"2020-01-18","arxiv_id":"2001.06627","repositories_listed":0,"syntology":null},{"url":null,"slug":"perception-and-decision-making-of-autonomous","title":"Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey","date":"2020-01-08","arxiv_id":"2001.02319","repositories_listed":0,"syntology":null}],"record_sha256":"c01b37115115339d6608e58aa923e88ffa3a140eea03868dfc5bc8586ef661e7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}