Real-time Dexterous Telemanipulation
Last updated
Last updated
This paper presents the End-Effects-Oriented Learning-Based Dexterous Telemanipulation (EFOLD) Framework, which aims to overcome the challenges faced in robotic telemanipulation by focusing on outcomes rather than mimicking human hand motions. Traditional approaches to telemanipulation usually map human hand gestures onto a robotic hand, often neglecting the complex physical interactions involved. EFOLD addresses these shortcomings by utilizing Deep Reinforcement Learning (DRL) and focusing on end effects—key features that represent the physical consequences of manipulation, such as force, tactile feedback, and movement.
This paper was presented at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), a prestigious international conference that gathers top researchers and practitioners from across the globe to discuss advances in robotics, automation, and intelligent systems. IROS is widely recognized as one of the most influential conferences in the field of robotics, showcasing cutting-edge research and serving as a platform for sharing groundbreaking ideas.
Key Highlights of the EFOLD Framework:
End-Effect Modeling: EFOLD redefines the telemanipulation process by treating the task as a Markov Game, where the human operator and robot are considered separate agents. The goal is to interpret the human operator's actions in terms of end effects that the robot must recreate.
Deep Reinforcement Learning: By using a learning-based approach, EFOLD enables the robotic hand to autonomously recreate the necessary interactions with objects, leading to precise and efficient manipulation without overburdening the human operator.
Human-Offline Training & Human-Online Testing: The framework adopts an innovative strategy where the DRL policy is trained offline, reducing the need for human input during training, and allowing humans to focus only on the testing phase.
The framework was evaluated using a virtual Shadow Robot Hand to perform dexterous manipulation tasks. Results show that EFOLD can achieve real-time control with low latency and high precision. During testing, EFOLD demonstrated its capability to replicate end effects efficiently, achieving a command-following latency of less than 0.11s and highly accurate tracking with an MSE of less than 0.084 rad.
Research Contributions:
Markov Game Model: The telemanipulation problem was formulated as a Markov Game, allowing for the integration of human and robotic agents under a unified mathematical model.
End Effect Categorization: Two categories of end effect extraction methods were proposed—internal and external—to enhance the interpretability and applicability of human commands.
Efficient Training Approach: The human-offline training strategy significantly saves time and reduces human effort during the training process.
This work sets a new benchmark for robot-assisted manipulation in environments where precision and real-time response are critical, such as telesurgery and remote exploration.
The paper and related materials, including implementation code, are available on GitHub.