VLM-driven Skill Selection for Robotic Assembly Tasks

This paper presents a robotic assembly framework that combines Vision-Language Models (VLMs) with imitation learning for assembly manipulation tasks. Our system employs a gripper-equipped robot that moves in 3D space to perform assembly operations. The framework integrates visual perception, natural language understanding, and learned primitive skills to enable flexible and adaptive robotic manipulation. Experimental results demonstrate the effectiveness of our approach in assembly scenarios, achieving high success rates while maintaining interpretability through the structured primitive skill decomposition.

Experimental Evaluation of Precise Placement of the Hollow Object with Asymmetric Pivot Manipulation

In this paper, we present asymmetric pivot manipulation for picking up rigid hollow objects to achieve a hole grasp. The pivot motion, executed by a position-controlled robotic arm, enables the gripper to effectively grasp hollow objects placed horizontally such that one gripper finger is positioned inside the object's hole, while the other contacts its outer surface along the length. Hole grasp is widely employed by humans to manipulate hollow objects, facilitating precise placement and enabling efficient subsequent operations, such as tightly packing objects into trays or accurately inserting them into narrow machine slots in manufacturing processes. Asymmetric pivoting for hole grasping is applicable to hollow objects of various sizes and hole shapes, including bottles, cups, and ducts. We investigate the variable parameters that satisfy the force balance conditions for successful grasping configurations. Our method can be implemented using a commercially available parallel-jaw gripper installed directly on a robot arm without modification. Experimental verification confirmed that hole grasp can be achieved using our proposed asymmetric pivot manipulation for various hollow objects, demonstrating a high success rate. Two use cases, namely aligning and feeding hollow cylindrical objects, were experimentally demonstrated on the testbed to clearly showcase the advantages of the hole grasp approach.

Visuo-Tactile Keypoint Correspondences for Object Manipulation

This paper presents a novel manipulation strategy that uses keypoint correspondences extracted from visuo-tactile sensor images to facilitate precise object manipulation. Our approach uses the visuo-tactile feedback to guide the robot's actions for accurate object grasping and placement, eliminating the need for post-grasp adjustments and extensive training. This method provides an improvement in deployment efficiency, addressing the challenges of manipulation tasks in environments where object locations are not predefined. We validate the effectiveness of our strategy through experiments demonstrating the extraction of keypoint correspondences and their application to real-world tasks such as block alignment and gear insertion, which require millimeter-level precision. The results show an average error margin significantly lower than that of traditional vision-based methods, which is sufficient to achieve the target tasks.