Teaching a robot to use electric tools with regrasp planning

Abstract

This study presents a straightforward method to teach robots to use tools. Teaching robots is crucial in quickly deploying and reconfiguring robots in next-generation factories. Conventional methods require third-party systems like wearable devices or complicated vision system to capture, analyse, and map human grasps, motion, and tool poses to robots. These systems assume lots of experience from their users. Unlike the conventional methods, this study does not involve learning human motion and skills. Instead, it only learns the object goal poses from the human user whilst employs regrasp planning to generate robot motion. The method is most suitable for a robot to learn the usage of electric tools that can be operated by simply switching on and off. The proposed method is validated using a dual-arm robot with hand-mounted cameras and several tools. Experimental results show that the proposed method is robust, feasible, and simple to teach robots. It can find a collision-free and kino-dynamic feasible grasp sequences and motion trajectories when the goal pose is reachable. The method allows the robot to automatically choose placements or handover considering the surrounding environment as intermediate states to change the pose of the tool and use tools following human demonstrations.

Towards Tethered Tool Manipulation Planning with the Help of a Tool Balancer

“…The simulation environment uses 3D meshes to represent the tool and the tool balancer in the robot environment. The tool original pose is determined with the robot hand-mounted cameras and the tool AR marker [30].…”

A Mechanical Screwing Tool for 2-Finger Parallel Grippers -- Design, Optimization, and Manipulation Policies

“…In [52], human demonstrations were captured by pose-tracking and were then employed to learn how to identify and use tools. Raessa et al [53] proposed a human demonstration-based method for teaching robots to use tools with special consideration of regrasp planning. Additionally, instead of demonstration, Xie et al [54] presented the method of using video prediction for reasoning the potential robot action to use the available objects as tools in an improvisational way.…”

Designing a Mechanical Tool for Robots With Two-Finger Parallel Grippers

“…A method was used to plan the motion of robots to reorient objects. Raessa et al [18] proposed a method to teach a dual-arm robot to use common electric tools. Grasp and regrasp planning were implemented to adjust the work pose of the tool following [19].…”

“…The minimum θ end can be computed using Equation (17). p should meet equations (18) and (19). h should meet equation (20).…”