Papers
959 papers found
Map-Adaptive Goal-Based Trajectory Prediction
Lingyao Zhang, Po-Hsun Su, Jerrick Hoang et al.
MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
Boris Ivanovic, Amine Elhafsi, Guy Rosman et al.
MELD: Meta-Reinforcement Learning from Images via Latent State Models
Zihao Zhao, Anusha Nagabandi, Kate Rakelly et al.
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
Neha Das, Sarah Bechtle, Todor Davchev et al.
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous
Rose Wang, J. Chase Kew, Dennis Lee et al.
Modeling Long-horizon Tasks as Sequential Interaction Landscapes
Soeren Pirk, Karol Hausman, Alexander Toshev et al.
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
Jun Yamada, Youngwoon Lee, Gautam Salhotra et al.
MuGNet: Multi-Resolution Graph Neural Network for Segmenting Large-Scale Pointclouds
Liuyue Xie, Tomotake Furuhata, Kenji Shimada
Multiagent Rollout and Policy Iteration for POMDP with Application to Multi-Robot Repair Problems
Sushmita Bhattacharya, Siva Kailas, Sahil Badyal et al.
Multi-Level Structure vs. End-to-End-Learning in High-Performance Tactile Robotic Manipulation
Florian Voigt, Lars Johannsmeier, Sami Haddadin
Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments
Tianchen Ji, Sri Theja Vuppala, Girish Chowdhary et al.
Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections
Junha Roh, Christoforos Mavrogiannis, Rishabh Madan et al.
MultiPoint: Cross-spectral registration of thermal and optical aerial imagery
Florian Achermann, Andrey Kolobov, Debadeepta Dey et al.
Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design
Jiankai Sun, Hao Sun, Tian Han et al.
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Ryan Julian, Benjamin Swanson, Gaurav Sukhatme et al.
One Thousand and One Hours: Self-driving Motion Prediction Dataset
John Houston, Guido Zuidhof, Luca Bergamini et al.
PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards
Prasoon Goyal, Scott Niekum, Raymond Mooney
Planning Paths Through Unknown Space by Imagining What Lies Therein
Yutao Han, Jacopo Banfi, Mark Campbell
PLAS: Latent Action Space for Offline Reinforcement Learning
Wenxuan Zhou, Sujay Bajracharya, David Held
PLOP: Probabilistic Polynomial Objects trajectory Prediction for autonomous driving
Thibault Buhet, Emilie Wirbel, Andrei Bursuc et al.
Policy learning in SE(3) action spaces
Dian Wang, Colin Kohler, Robert Platt
Positive-Unlabeled Reward Learning
Danfei Xu, Misha Denil
Probably Approximately Correct Vision-Based Planning using Motion Primitives
Sushant Veer, Anirudha Majumdar
Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection
Alex Bewley, Pei Sun, Thomas Mensink et al.
Reactive motion planning with probabilisticsafety guarantees
Yuxiao Chen, Ugo Rosolia, Chuchu Fan et al.