Papers
Deep Value Model Predictive Control
David Hoeller, Farbod Farshidian, Marco Hutter
Graph-Structured Visual Imitation
Maximilian Sieb, Zhou Xian, Audrey Huang et al.
Locally Weighted Regression Pseudo-Rehearsal for Adaptive Model Predictive Control
Grady R. Williams, Brian Goldfain, Keuntaek Lee et al.
Learning value functions with relational state representations for guiding task-and-motion planning
Beomjoon Kim, Luke Shimanuki
Quasi-Newton Trust Region Policy Optimization
Devesh K. Jha, Arvind U. Raghunathan, Diego Romeres
Task-Conditioned Variational Autoencoders for Learning Movement Primitives
Michael Noseworthy, Rohan Paul, Subhro Roy et al.
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds
Yin Zhou, Pei Sun, Yu Zhang et al.
MAME : Model-Agnostic Meta-Exploration
Swaminathan Gurumurthy, Sumit Kumar, Katia Sycara
Counter-example Guided Learning of Bounds on Environment Behavior
Yuxiao Chen, Sumanth Dathathri, Tung Phan-Minh et al.
RoboNet: Large-Scale Multi-Robot Learning
Sudeep Dasari, Frederik Ebert, Stephen Tian et al.
Disentangled Relational Representations for Explaining and Learning from Demonstration
Yordan Hristov, Daniel Angelov, Michael Burke et al.
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning
Kevin Sebastian Luck, Heni Ben Amor, Roberto Calandra
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
Rémy Portelas, Cédric Colas, Katja Hofmann et al.
Graph Policy Gradients for Large Scale Robot Control
Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro et al.
Learning Compact Models for Planning with Exogenous Processes
Rohan Chitnis, Tomás Lozano-Pérez
Learning to Navigate Using Mid-Level Visual Priors
Alexander Sax, Jeffrey O. Zhang, Bradley Emi et al.
Multi-Agent Reinforcement Learning with Multi-Step Generative Models
Orr Krupnik, Igor Mordatch, Aviv Tamar
Energy-efficient Path Planning for Ground Robots by and Combining Air and Ground Measurements
Minghan Wei, Volkan Isler
Learning from My Partner’s Actions: Roles in Decentralized Robot Teams
Dylan P. Losey, Mengxi Li, Jeannette Bohg et al.
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
Michael Neunert, Abbas Abdolmaleki, Markus Wulfmeier et al.
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
Andrey Kurenkov, Ajay Mandlekar, Roberto Martin-Martin et al.
Stochastic Optimal Control as Approximate Input Inference
Joe Watson, Hany Abdulsamad, Jan Peters
Provably Robust Blackbox Optimization for Reinforcement Learning
Krzysztof Choromanski, Aldo Pacchiano, Jack Parker-Holder et al.
Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks
Ekaterina Tolstaya, Fernando Gama, James Paulos et al.
Connectivity Guaranteed Multi-robot Navigation via Deep Reinforcement Learning
Juntong Lin, Xuyun Yang, Peiwei Zheng et al.