Problem
Research questions and friction points this paper is trying to address.
in-hand manipulation
dexterous manipulation
parallel-jaw gripper
degrees of freedom
robotic gripper
Belt-Finger: An Affordable Soft Belt-Driven Gripper for Dexterous In-Hand Manipulation
🤖 AI Summary
This work addresses the limited in-hand manipulation capability of conventional parallel grippers, which struggle to perform dexterous tasks in confined spaces. The authors propose an enhanced gripper integrating dual soft-band-driven finger modules that preserve the original open-close functionality while introducing three additional in-hand degrees of freedom—translation, pitch, and roll. The design is structurally simple, low-cost, easily integrable, and maintains the reliability and control precision of traditional grippers. By combining model predictive control (MPC), a lightweight teleoperation interface, and policy learning, the system achieves efficient coordinated control over ten degrees of freedom. Experimental results demonstrate that the gripper significantly improves dexterity and task feasibility across a range of complex manipulation scenarios, confirming its general effectiveness under diverse control paradigms.
📝 Abstract
Parallel-jaw grippers are the default manipulator choice in robotics because they are simple, robust, and inexpensive. Their limited in-hand mobility, however, often forces large arm motions and restricts dexterous manipulation in confined workspaces. We present a parallel-gripper upgrade: a double-soft-belt-based finger module that preserves standard opening/closing while adding three in-hand degrees of freedom (DoF): translation, pitch, and roll. The mechanism is deliberately kept simple and engineered for inexpensive manufacturing and straightforward integration, preserving the reliability and precise control of traditional parallel grippers while greatly broadening the range of manipulation capabilities. To demonstrate the utility of the added DoFs, we integrate the gripper in two control pipelines. First, we adapt a model predictive controller for in-hand manipulation of known objects. Second, we introduce a lightweight teleoperation interface that enables simultaneous control of the robot arm and gripper (10 DoFs total) with minimal hardware. Across a suite of challenging manipulation tasks executed via teleoperation, MPC, and trained policies, the proposed gripper consistently improves dexterity and task feasibility compared to a conventional parallel gripper
Innovation
Methods, ideas, or system contributions that make the work stand out.
soft belt-driven gripper
in-hand manipulation
dexterous grasping
affordable robotic hand
multi-DoF finger
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