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Found 8 papers, 0 papers with code
Confidence-aware 3D Gaze Estimation and Evaluation Metric
To address the unreliable and overconfident issues, we introduce a confidence-aware model that predicts uncertainties together with gaze angle estimations.
Multi-Phase Multi-Objective Dexterous Manipulation with Adaptive Hierarchical Curriculum
Dexterous manipulation tasks usually have multiple objectives, and the priorities of these objectives may vary at different phases of a manipulation task.
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping
In this work, we develop a novel Physics-Guided Deep Reinforcement Learning with a Hierarchical Reward Mechanism to improve learning efficiency and generalizability for learning-based autonomous grasping.
Forming Real-World Human-Robot Cooperation for Tasks With General Goal
In human-robot cooperation, the robot cooperates with humans to accomplish the task together.
Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
The simulation results shows that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density.
Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation
Enabling robots to provide effective assistance yet still accommodating the operator's commands for telemanipulation of an object is very challenging because robot's assistive action is not always intuitive for human operators and human behaviors and preferences are sometimes ambiguous for the robot to interpret.
Learn Task First or Learn Human Partner First: A Hierarchical Task Decomposition Method for Human-Robot Cooperation
Applying Deep Reinforcement Learning (DRL) to Human-Robot Cooperation (HRC) in dynamic control problems is promising yet challenging as the robot needs to learn the dynamics of the controlled system and dynamics of the human partner.
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.
