Search Results for author:
Found 7 papers, 3 papers with code
CasIL: Cognizing and Imitating Skills via a Dual Cognition-Action Architecture
Heuristically, we extend the usual notion of action to a dual Cognition (high-level)-Action (low-level) architecture by introducing intuitive human cognitive priors, and propose a novel skill IL framework through human-robot interaction, called Cognition-Action-based Skill Imitation Learning (CasIL), for the robotic agent to effectively cognize and imitate the critical skills from raw visual demonstrations.
Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective
As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment.
Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning
Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, state-of-the-art DRL algorithms still struggle to learn long-horizon, multi-step and sparse reward tasks, such as stacking several blocks given only a task-completion reward signal.
An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet
This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine.
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player
An efficient training strategy is proposed to allow a robot to learn from both human experience data and self-exploratory data.
3D Vision-guided Pick-and-Place Using Kuka LBR iiwa Robot
This paper presents the development of a control system for vision-guided pick-and-place tasks using a robot arm equipped with a 3D camera.
Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration
The results show that the proposed network is capable to map low-end shock signals to its high-end counterparts with satisfactory accuracy.
