Robotics

Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions (Student Abstract) AAAI 2020 Multi-Agent Routing Value Iteration Network ICML 2020 Multi-Level Structure vs. End-to-End-Learning in High-Performance Tactile Robotic Manipulation CoRL 2020 Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Objects AAAI 2020 MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics IJCAI 2020 Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch UAI 2020 Neural Dynamic Policies for End-to-End Sensorimotor Learning NeurIPS 2020 Neurosymbolic Reinforcement Learning with Formally Verified Exploration NeurIPS 2020 Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning CoRL 2020 Nonlinear Model Predictive Control of Robotic Systems with Control Lyapunov Functions RSS 2020 Object Goal Navigation using Goal-Oriented Semantic Exploration NeurIPS 2020 One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control ICML 2020 Online Decision Based Visual Tracking via Reinforcement Learning NeurIPS 2020 PackIt: A Virtual Environment for Geometric Planning ICML 2020 PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation CVPR 2020 PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards CoRL 2020 PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals NeurIPS 2020 Planning to Explore via Self-Supervised World Models ICML 2020 PLAS: Latent Action Space for Offline Reinforcement Learning CoRL 2020 Policy learning in SE(3) action spaces CoRL 2020 Policy Search by Target Distribution Learning for Continuous Control AAAI 2020 POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis NeurIPS 2020 POMO: Policy Optimization with Multiple Optima for Reinforcement Learning NeurIPS 2020 Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework NeurIPS 2020 POPCORN: Partially Observed Prediction Constrained Reinforcement Learning AISTATS 2020