CONTHER: Human-Like Contextual Robot Learning via Hindsight Experience Replay and Transformers without Expert Demonstrations

This paper presents CONTHER, a novel reinforcement learning algorithm designed to efficiently and rapidly train robotic agents for goal-oriented manipulation tasks and obstacle avoidance. The algorithm uses a modified replay buffer inspired by the Hindsight Experience Replay (HER) approach to artificially populate experience with successful trajectories, effectively addressing the problem of sparse reward scenarios and eliminating the need to manually collect expert demonstrations. The developed algorithm proposes a Transformer-based architecture to incorporate the context of previous states, allowing the agent to perform a deeper analysis and make decisions in a manner more akin to human learning. The effectiveness of the built-in replay buffer, which acts as an "internal demonstrator", is twofold: it accelerates learning and allows the algorithm to adapt to different tasks. Empirical data confirm the superiority of the algorithm by an average of 38.46% over other considered methods, and the most successful baseline by 28.21%, showing higher success rates and faster convergence in the point-reaching task. Since the control is performed through the robot's joints, the algorithm facilitates potential adaptation to a real robot system and construction of an obstacle avoidance task. Therefore, the algorithm has also been tested on tasks requiring following a complex dynamic trajectory and obstacle avoidance. The design of the algorithm ensures its applicability to a wide range of goal-oriented tasks, making it an easily integrated solution for real-world robotics applications.

Robotic framework for autonomous manipulation of laboratory equipment with different degrees of transparency via 6D pose estimation

Many modern robotic systems operate autonomously, however they often lack the ability to accurately analyze the environment and adapt to changing external conditions, while teleoperation systems often require special operator skills. In the field of laboratory automation, the number of automated processes is growing, however such systems are usually developed to perform specific tasks. In addition, many of the objects used in this field are transparent, making it difficult to analyze them using visual channels. The contributions of this work include the development of a robotic framework with autonomous mode for manipulating liquid-filled objects with different degrees of transparency in complex pose combinations. The conducted experiments demonstrated the robustness of the designed visual perception system to accurately estimate object poses for autonomous manipulation, and confirmed the performance of the algorithms in dexterous operations such as liquid dispensing. The proposed robotic framework can be applied for laboratory automation, since it allows solving the problem of performing non-trivial manipulation tasks with the analysis of object poses of varying degrees of transparency and liquid levels, requiring high accuracy and repeatability.