{"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/6","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":6,"pages_in_order":10,"rows_per_page":100,"rows":[501,600],"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/5","next":"/task/motion-planning/papers/7","papers":[{"url":null,"slug":"spatial-intelligence-of-a-self-driving-car","title":"Spatial Intelligence of a Self-driving Car and Rule-Based Decision Making","date":"2023-08-02","arxiv_id":"2308.01085","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-implicit-behavior-cloning-and-dynamic","title":"Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning","date":"2023-07-29","arxiv_id":"2307.16062","repositories_listed":0,"syntology":null},{"url":null,"slug":"traffic-flow-simulation-for-autonomous","title":"Traffic Flow Simulation for Autonomous Driving","date":"2023-07-23","arxiv_id":"2307.16762","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-for-data-efficient","title":"Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review","date":"2023-07-16","arxiv_id":"2307.08024","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-heuristic-search-based-motion","title":"A Multi-Heuristic Search-based Motion Planning for Automated Parking","date":"2023-07-15","arxiv_id":"2307.07857","repositories_listed":0,"syntology":null},{"url":null,"slug":"cart-collision-avoidance-and-robust-tracking","title":"CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems","date":"2023-07-13","arxiv_id":"2307.08602","repositories_listed":0,"syntology":null},{"url":null,"slug":"embodied-lifelong-learning-for-task-and","title":"Embodied Lifelong Learning for Task and Motion Planning","date":"2023-07-13","arxiv_id":"2307.06870","repositories_listed":0,"syntology":null},{"url":null,"slug":"robotic-surface-exploration-with-vision-and","title":"Robotic surface exploration with vision and tactile sensing for cracks detection and characterisation","date":"2023-07-13","arxiv_id":"2307.06784","repositories_listed":0,"syntology":null},{"url":null,"slug":"rh20t-a-robotic-dataset-for-learning-diverse","title":"RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot","date":"2023-07-02","arxiv_id":"2307.00595","repositories_listed":0,"syntology":null},{"url":null,"slug":"collision-free-motion-planning-for-mobile","title":"Collision-free Motion Planning for Mobile Robots by Zero-order Robust Optimization-based MPC","date":"2023-06-30","arxiv_id":"2306.17445","repositories_listed":0,"syntology":null},{"url":null,"slug":"imposition-implicit-backdoor-attack-through","title":"Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving","date":"2023-06-27","arxiv_id":"2306.15755","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-transfer-learning-for-intelligent","title":"Deep Transfer Learning for Intelligent Vehicle Perception: a Survey","date":"2023-06-26","arxiv_id":"2306.15110","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimsam-diffusion-models-as-samplers-for-task","title":"DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability","date":"2023-06-22","arxiv_id":"2306.13196","repositories_listed":0,"syntology":null},{"url":null,"slug":"robot-learning-with-sensorimotor-pre-training","title":"Robot Learning with Sensorimotor Pre-training","date":"2023-06-16","arxiv_id":"2306.10007","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcement-learning-in-robotic-motion","title":"Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning","date":"2023-06-11","arxiv_id":"2306.06754","repositories_listed":0,"syntology":null},{"url":null,"slug":"vehicle-dynamics-modeling-for-autonomous","title":"Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes","date":"2023-06-06","arxiv_id":"2306.03405","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-sampling-dictionaries-for-efficient","title":"Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with Transformers","date":"2023-06-01","arxiv_id":"2306.00851","repositories_listed":0,"syntology":null},{"url":null,"slug":"tofg-a-unified-and-fine-grained-environment","title":"TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving","date":"2023-05-31","arxiv_id":"2305.20068","repositories_listed":0,"syntology":null},{"url":"/paper/large-car-following-data-based-on-lyft-level","slug":"large-car-following-data-based-on-lyft-level","title":"Large Car-following Data Based on Lyft level-5 Open Dataset: Following Autonomous Vehicles vs. Human-driven Vehicles","date":"2023-05-30","arxiv_id":"2305.18921","repositories_listed":0,"syntology":null},{"url":null,"slug":"imitating-task-and-motion-planning-with","title":"Imitating Task and Motion Planning with Visuomotor Transformers","date":"2023-05-25","arxiv_id":"2305.16309","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-planning-for-parabolic-equations-using","title":"Motion planning for parabolic equations using flatness and finite-difference approximations","date":"2023-05-21","arxiv_id":"2305.12404","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-difficulty-of-intersection-checking","title":"On the Difficulty of Intersection Checking with Polynomial Zonotopes","date":"2023-05-17","arxiv_id":"2305.09901","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-motion-planning-with-environment","title":"Safe motion planning with environment uncertainty","date":"2023-05-10","arxiv_id":"2305.06004","repositories_listed":0,"syntology":null},{"url":null,"slug":"ibbt-informed-batch-belief-trees-for-motion","title":"IBBT: Informed Batch Belief Trees for Motion Planning Under Uncertainty","date":"2023-04-21","arxiv_id":"2304.10984","repositories_listed":0,"syntology":null},{"url":null,"slug":"topological-guided-actor-critic-modular","title":"Topological Guided Actor-Critic Modular Learning of Continuous Systems with Temporal Objectives","date":"2023-04-20","arxiv_id":"2304.10041","repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-reinforcement-learning-based","title":"Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving","date":"2023-04-17","arxiv_id":"2304.08280","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-based-reactive-synthesis-for","title":"Sampling-based Reactive Synthesis for Nondeterministic Hybrid Systems","date":"2023-04-14","arxiv_id":"2304.06876","repositories_listed":0,"syntology":null},{"url":null,"slug":"lidar-based-3d-tracking-and-state-estimation","title":"Lidar based 3D Tracking and State Estimation of Dynamic Objects","date":"2023-04-03","arxiv_id":"2304.01396","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthesis-of-opacity-enforcing-winning","title":"Synthesis of Opacity-Enforcing Winning Strategies Against Colluded Opponent","date":"2023-04-03","arxiv_id":"2304.01286","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-learning-based-adaptive-compliance-method","title":"A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation","date":"2023-03-27","arxiv_id":"2303.15262","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-task-and-motion-planning-and","title":"Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments","date":"2023-03-27","arxiv_id":"2303.14874","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-motion-planner-for-urban","title":"Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning","date":"2023-03-24","arxiv_id":"2303.13986","repositories_listed":0,"syntology":null},{"url":null,"slug":"planning-for-complex-non-prehensile","title":"Planning for Complex Non-prehensile Manipulation Among Movable Objects by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation","date":"2023-03-23","arxiv_id":"2303.13352","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-hybrid-learning-framework-for","title":"A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction","date":"2023-03-22","arxiv_id":"2303.12274","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-object-removal-for-effective-slam","title":"Dynamic Object Removal for Effective Slam","date":"2023-03-20","arxiv_id":"2303.10923","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-human-robot-collaboration-via","title":"Improving Human-Robot Collaboration via Computational Design","date":"2023-03-20","arxiv_id":"2303.11425","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-federated-learning-for-connected","title":"A Survey of Federated Learning for Connected and Automated Vehicles","date":"2023-03-19","arxiv_id":"2303.10677","repositories_listed":0,"syntology":null},{"url":null,"slug":"path-planning-for-autonomous-driving-the","title":"Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives","date":"2023-03-17","arxiv_id":"2303.09824","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-learning-of-high-level-plans-from","title":"Efficient Learning of High Level Plans from Play","date":"2023-03-16","arxiv_id":"2303.09628","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-multiple-algorithm-configurations","title":"Discovering Multiple Algorithm Configurations","date":"2023-03-13","arxiv_id":"2303.07434","repositories_listed":0,"syntology":null},{"url":null,"slug":"importance-filtering-with-risk-models-for","title":"Importance Filtering with Risk Models for Complex Driving Situations","date":"2023-03-13","arxiv_id":"2303.06935","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-of-velocity-ramps-with-survival","title":"Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins","date":"2023-03-13","arxiv_id":"2303.07047","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-occupancy-predictive-representations-for","title":"Deep Occupancy-Predictive Representations for Autonomous Driving","date":"2023-03-07","arxiv_id":"2303.04218","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-skill-acquisition-for-complex","title":"Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments","date":"2023-03-06","arxiv_id":"2303.03365","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-scene-embeddings-for-gradient","title":"Leveraging Scene Embeddings for Gradient-Based Motion Planning in Latent Space","date":"2023-03-06","arxiv_id":"2303.03364","repositories_listed":0,"syntology":null},{"url":null,"slug":"planning-and-control-of-uncertain-cooperative","title":"Planning and Control of Uncertain Cooperative Mobile Manipulator-Endowed Systems under Temporal-Logic Tasks","date":"2023-03-02","arxiv_id":"2303.01379","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-motion-plans-for-articulating","title":"Predicting Motion Plans for Articulating Everyday Objects","date":"2023-03-02","arxiv_id":"2303.01484","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-tube-based-non-gaussian-risk","title":"Real-Time Tube-Based Non-Gaussian Risk Bounded Motion Planning for Stochastic Nonlinear Systems in Uncertain Environments via Motion Primitives","date":"2023-03-02","arxiv_id":"2303.01631","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cooperation-aware-lane-change-method-for-1","title":"A Cooperation-Aware Lane Change Method for Automated Vehicles","date":"2023-03-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lqr-trees-with-sampling-based-exploration-of","title":"LQR-trees with Sampling Based Exploration of the State Space","date":"2023-03-01","arxiv_id":"2303.00553","repositories_listed":0,"syntology":null},{"url":null,"slug":"vandermonde-trajectory-bounds-for-linear","title":"Vandermonde Trajectory Bounds for Linear Companion Systems","date":"2023-02-21","arxiv_id":"2302.10995","repositories_listed":0,"syntology":null},{"url":null,"slug":"computational-tradeoff-in-minimum-obstacle","title":"Computational Tradeoff in Minimum Obstacle Displacement Planning for Robot Navigation","date":"2023-02-14","arxiv_id":"2302.07114","repositories_listed":0,"syntology":null},{"url":null,"slug":"holistic-deep-reinforcement-learning-based","title":"Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems","date":"2023-02-06","arxiv_id":"2302.02921","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-object-search","title":"Generalized Object Search","date":"2023-01-24","arxiv_id":"2301.10121","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-view-decision-transformers-for","title":"Learning to View: Decision Transformers for Active Object Detection","date":"2023-01-23","arxiv_id":"2301.09544","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-motion-sickness-with-optimization","title":"Mitigating Motion Sickness with Optimization-based Motion Planning","date":"2023-01-19","arxiv_id":"2301.07977","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-distance-geometric-method-for-recovering","title":"A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image","date":"2023-01-05","arxiv_id":"2301.02051","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantitative-planning-with-action-deception","title":"Quantitative Planning with Action Deception in Concurrent Stochastic Games","date":"2023-01-03","arxiv_id":"2301.01349","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-zero-domain-gap-a-comprehensive-study","title":"Towards Zero Domain Gap: A Comprehensive Study of Realistic LiDAR Simulation for Autonomy Testing","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-safety-and-robustness-guarantees","title":"Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems","date":"2022-12-13","arxiv_id":"2212.06874","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-synthetic-off-policy-experience-for","title":"Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula","date":"2022-12-02","arxiv_id":"2212.01375","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonlinear-controllability-and-function","title":"Nonlinear controllability and function representation by neural stochastic differential equations","date":"2022-12-01","arxiv_id":"2212.00896","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-agile-paths-from-optimal-control","title":"Learning Agile Paths from Optimal Control","date":"2022-11-30","arxiv_id":"2212.00184","repositories_listed":0,"syntology":null},{"url":"/paper/r-pred-two-stage-motion-prediction-via-tube","slug":"r-pred-two-stage-motion-prediction-via-tube","title":"R-Pred: Two-Stage Motion Prediction Via Tube-Query Attention-Based Trajectory Refinement","date":"2022-11-16","arxiv_id":"2211.08609","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-the-integration-of-machine","title":"A Survey on the Integration of Machine Learning with Sampling-based Motion Planning","date":"2022-11-15","arxiv_id":"2211.08368","repositories_listed":0,"syntology":null},{"url":null,"slug":"rl-dwa-omnidirectional-motion-planning-for","title":"RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring","date":"2022-11-09","arxiv_id":"2211.04993","repositories_listed":0,"syntology":null},{"url":null,"slug":"robot-basics-representation-rotation-and","title":"Robot Basics: Representation, Rotation and Velocity","date":"2022-11-05","arxiv_id":"2211.02786","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-vehicle-highway-merging-motion","title":"Automated Vehicle Highway Merging: Motion Planning via Adaptive Interactive Mixed-Integer MPC","date":"2022-11-04","arxiv_id":"2211.02225","repositories_listed":0,"syntology":null},{"url":null,"slug":"p4p-conflict-aware-motion-prediction-for","title":"P4P: Conflict-Aware Motion Prediction for Planning in Autonomous Driving","date":"2022-11-03","arxiv_id":"2211.01634","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-based-plan-feasibility-prediction","title":"Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning","date":"2022-11-03","arxiv_id":"2211.01576","repositories_listed":0,"syntology":null},{"url":null,"slug":"environment-aware-interactive-movement","title":"Environment-aware Interactive Movement Primitives for Object Reaching in Clutter","date":"2022-10-28","arxiv_id":"2210.16194","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-hot-a-modular-hierarchical-approach-for","title":"Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport","date":"2022-10-28","arxiv_id":"2210.15908","repositories_listed":0,"syntology":null},{"url":null,"slug":"policy-guided-lazy-search-with-feedback-for","title":"Policy-Guided Lazy Search with Feedback for Task and Motion Planning","date":"2022-10-25","arxiv_id":"2210.14055","repositories_listed":0,"syntology":null},{"url":null,"slug":"guided-skill-learning-and-abstraction-for","title":"LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation","date":"2022-10-23","arxiv_id":"2210.12631","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-constrained-imitation-learning","title":"Differentiable Constrained Imitation Learning for Robot Motion Planning and Control","date":"2022-10-21","arxiv_id":"2210.11796","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-guiding-spaces-for-motion-planning","title":"Evaluating Guiding Spaces for Motion Planning","date":"2022-10-16","arxiv_id":"2210.08640","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-motion-planning-in-dynamic","title":"Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding","date":"2022-10-16","arxiv_id":"2210.08408","repositories_listed":0,"syntology":null},{"url":null,"slug":"robot-navigation-anticipative-strategies-in","title":"Robot Navigation Anticipative Strategies in Deep Reinforcement Motion Planning","date":"2022-10-15","arxiv_id":"2210.08280","repositories_listed":0,"syntology":null},{"url":null,"slug":"socialmapf-optimal-and-efficient-multi-agent","title":"SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation","date":"2022-10-15","arxiv_id":"2210.08390","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-multi-robot-motion-planning-for","title":"Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance","date":"2022-10-13","arxiv_id":"2210.07141","repositories_listed":0,"syntology":null},{"url":null,"slug":"experiment-design-for-identification-of","title":"Experiment Design for Identification of Marine Models","date":"2022-10-11","arxiv_id":"2210.05496","repositories_listed":0,"syntology":null},{"url":null,"slug":"hypergraph-based-multi-robot-task-and-motion","title":"Hypergraph-based Multi-Robot Task and Motion Planning","date":"2022-10-09","arxiv_id":"2210.04333","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-planning-on-visual-manifolds","title":"Motion Planning on Visual Manifolds","date":"2022-10-08","arxiv_id":"2210.04047","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-localization-using-bernstein","title":"On-line Motion Planning Using Bernstein Polynomials for Enhanced Target Localization in Autonomous Vehicles","date":"2022-10-06","arxiv_id":"2210.03187","repositories_listed":0,"syntology":null},{"url":null,"slug":"opportunistic-qualitative-planning-in-1","title":"Opportunistic Qualitative Planning in Stochastic Systems with Incomplete Preferences over Reachability Objectives","date":"2022-10-04","arxiv_id":"2210.01878","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-knowledge-graph-based-methods-for","title":"A Survey on Knowledge Graph-based Methods for Automated Driving","date":"2022-09-30","arxiv_id":"2210.08119","repositories_listed":0,"syntology":null},{"url":null,"slug":"obstacle-identification-and-ellipsoidal","title":"Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments","date":"2022-09-28","arxiv_id":"2209.14233","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-lattice-based-motion-planning","title":"Robust Lattice-based Motion Planning","date":"2022-09-28","arxiv_id":"2209.14360","repositories_listed":0,"syntology":null},{"url":null,"slug":"intercepting-a-flying-target-while-avoiding","title":"Unified Control Framework for Real-Time Interception and Obstacle Avoidance of Fast-Moving Objects with Diffusion Variational Autoencoder","date":"2022-09-27","arxiv_id":"2209.13628","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-vehicle-coordination-the","title":"Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models","date":"2022-09-19","arxiv_id":"2209.08763","repositories_listed":0,"syntology":null},{"url":null,"slug":"case-studies-for-computing-density-of","title":"Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning","date":"2022-09-16","arxiv_id":"2209.08073","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-perception-planning-and-control","title":"Efficient Perception, Planning, and Control Algorithm for Vision-Based Automated Vehicles","date":"2022-09-15","arxiv_id":"2209.07042","repositories_listed":0,"syntology":null},{"url":null,"slug":"camo-mot-combined-appearance-motion","title":"CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion","date":"2022-09-06","arxiv_id":"2209.02540","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathematical-certification-of-motion-planning","title":"Mathematical certification of motion planning on uncertain terrain with limited perception: a case study","date":"2022-08-30","arxiv_id":"2208.13961","repositories_listed":0,"syntology":null},{"url":null,"slug":"robot-motion-planning-as-video-prediction-a","title":"Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner","date":"2022-08-24","arxiv_id":"2208.11287","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-to-green-predictions-for-fully-actuated","title":"Time-to-Green predictions for fully-actuated signal control systems with supervised learning","date":"2022-08-24","arxiv_id":"2208.11344","repositories_listed":0,"syntology":null},{"url":null,"slug":"intertrack-interaction-transformer-for-3d","title":"InterTrack: Interaction Transformer for 3D Multi-Object Tracking","date":"2022-08-17","arxiv_id":"2208.08041","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-reinforcement-learning-for-6","title":"Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot","date":"2022-08-01","arxiv_id":"2208.01160","repositories_listed":0,"syntology":null},{"url":null,"slug":"motion-planning-and-control-for-multi-vehicle","title":"Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds","date":"2022-07-22","arxiv_id":"2207.11136","repositories_listed":0,"syntology":null},{"url":null,"slug":"world-robot-challenge-2020-partner-robot-a","title":"World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator","date":"2022-07-20","arxiv_id":"2207.10106","repositories_listed":0,"syntology":null}],"record_sha256":"3ba020fe49e8cb6300e99ca77445bba7048ca80c1c7fad8b83f21c7fc2a6dfaa","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}