Koala Gripper: Co-designing Robotic Grippers and Data-Capture Devices for Scaling Dexterous Manipulation Learning

Amar Hajj-Ahmad; Zubin Kremer Guha; Tim Fofonoff; Zhi Ern Teoh; Ciarán T. O'Neill; Ben Thacher; Igor Fala; Vidullan Surendran

cs.ROAug 20, 2026

Koala Gripper: Co-designing Robotic Grippers and Data-Capture Devices for Scaling Dexterous Manipulation Learning

Authors: Amar Hajj-Ahmad, Zubin Kremer Guha, Tim Fofonoff, Zhi Ern Teoh, Ciarán T. O'Neill, Ben Thacher, Igor Fala, Vidullan Surendran,

Organizations: * These authors contributed equally to this work.

Abstract

As the demand for larger manipulation datasets grows, handheld robotic gripper data collection and the associated gripper designs become more vital. Current data collection device designs trend towards matching the morphologies of existing robotic grippers, sacrificing ergonomics and manipulation performance. In this paper, we propose a co-design framework that guides the simultaneous development of both data collection and robotic execution devices by weaving both platform constraints into the design process. Through this workflow, we present the Koala Gripper system, a data capture device and robotic gripper platform that improves dexterity and grasp capability compared to parallel jaw grippers while preserving scalability and ease-of-use. The design introduces a novel force-optimized finger/trigger linkage mechanism with directional reflected mass characteristics, a unique monolithic dual-thumb, and user-centered ergonomic design. The design's actuated robotic fingers are backdrivable, with effective mass on the order of tens of grams. We show that these grippers are capable of secure grasps over a wide range of objects, forceful tool use, and precise singulation. We further validate the platform by deploying it with an end-to-end data collection and policy execution pipeline that highlights its capabilities through learning from demonstration. More information available at http://koalagripper.rai-inst.com

Figures & tables

Fig. 1: The co-designed Koala Data Capture Device (a) and Robot Device (b) demonstrate a bolt fastening task using a standard drill.

Fig. 2: Task requirements, along with constraints and design targets from the CD and RD, feed common features and elements. These are then propagated back to each device, creating iterative cycles throughout the co-design process.

Fig. 3: The space around a robotic gripper can be segmented into several key active workspace areas that define how the gripper interacts with the environment and the associated design implications.

Fig. 4: The finger linkage mechanism integrates the common finger linkage (i) with the RD actuation mechanism (ii) and the CD trigger mechanism (iii)

Fig. 5: The path traced by the proposed trigger mechanism closely mimics natural fingertip closing paths [ 29 ] , improving energy harvesting performance.

Fig. 6: Mechanical advantage of the finger (top), trigger (middle), and combined (bottom) mechanism as configured in the CD. Augmenting the two mechanisms creates consistent mechanical advantage over the full stroke.

Fig. 7: The full workspace of the Koala gripper spans from 0mm pinch grasps to power grasps covering a 7 mm – 100 mm diameter range. Pad geometry targets power grasps of a cylindrical 44 mm diameter standard handle [ 30 ] .

Fig. 8: Triggers for the thumb (1), index finger (2), and middle finger (3) map human actuation to the gripper fingers and thumb. Stabilizing ergonomic features include a dorsal cushion (A), a ventral cushion (B), and a thumb-trigger side load support (C).

Fig. 9: Three wide-angle cameras are positioned on the gripper to observe the gripper fingers and the scene from multiple angles with minimal occlusions.

Fig. 10: Belted ellipsoid (a) and path-aligned (b) representations of finger effective mass over its complete stroke.

Thickness	CD	RD	Difference
10 mm	$25.88\pm 0.64N$	$25.06\pm 0.45N$	$-3.17\%\pm 4.25\%$
20 mm	$23.84\pm 0.73N$	$24.88\pm 0.91N$	$4.36\%\pm 6.73\%$
30 mm	$24.41\pm 0.65N$	$26.39\pm 0.88N$	$8.11\%\pm 6.00\%$
40 mm	$24.34\pm 0.77N$	$26.81\pm 1.16N$	$10.15\%\pm 7.50\%$

TABLE I: CD vs RD fingertip grasp forces

Fig. 11: Koala grasps the following objects: (a) nail † , (b) key † , (c) pencil (pinch), (d) card, (e) plate † , (f) bowl † , (g) mug † , (h) jug † , (i) box, (j) 25 lb sandbag, (k) pencil (power), (l) coffee can † , (m) 2 L bottle, (n) apple † , (o) ball † , (p) spoon † , (q) hammer † , (r) spray bottle † , (s) drill † . ( † From YCB object set [ 31 ] )

Robotic Gripper Design

See all 13 papers

YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale
Jun 8, 2026Takehiko Ohkawa, Jumpei Arima, Yuki Noguchi +16Dexterous Manipulation Robot Manipulation Datasets
1AI Robot Association (AIRoA) · 2Toyota Motor Corporation · 4The University of Tokyo +5
House of Dextra: Cross-embodied Co-design for Dexterous Hands
Dec 3, 2025Kehlani Fay, Darin Anthony Djapri, Anya Zorin +6Robotic Hand Design Dexterous Manipulation
University of California, San Diego · University of California, Santa Barbara
AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection
Jun 22, 2026Mingi Choi, Gunhee Kim, Jisoo Kim +4Robotic Grasping Robotic Data Collection
Seoul National University · RLWRLD

Jun 8, 2026J/K move · Enter open · S save

YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale

Takehiko Ohkawa, Jumpei Arima, Yuki Noguchi, Masatoshi Tateno+15

1AI Robot Association (AIRoA) · 2Toyota Motor Corporation · 4The University of Tokyo+5

Dexterous Manipulation Robot Manipulation Datasets Bimanual Robotic Manipulation Robotic Data Collection Robot Teleoperation Robotic Gripper Design

Open paper AskPDF arXiv

We introduce Yielding Universal Bidigital Interface (YUBI), a finger-aligned gripper designed to enable intuitive, ergonomic, and scalable data collection for bimanual dexterous manipulation. While handheld data collection systems such as Universal Manipulation Interface (UMI) enable affordable data collection, their bulky pistol-grip designs can pose ergonomic and usability challenges for fine-grained, dexterous manipulation tasks. To address this, YUBI presents a distinct design principle: yielding, finger-driven actuation that directly maps human finger movements to gripper jaw motion. Using the YUBI devices, we set up a data collection system with integrated VR-based 6 DoF tracking of the gripper, ensuring high-fidelity trajectory data acquisition. We curate a UMI-based dataset of unprecedented scale: 8,434 hours across 1.20M episodes and 119 tasks. Experiments show that YUBI offers advantages over the UMI gripper in versatility for complex bimanual tasks, dexterity, and operational efficiency. A single policy trained on the YUBI dataset transfers across multiple bimanual robots (UR, Franka, and ELEY) simply by mounting the gripper on each platform, confirming that the collected data are directly executable as policy supervision. We release the gripper hardware, data-collection software, and dataset as one integrated stack, offering the open community a reproducible path to large-scale data acquisition for advancing robotic foundation models.

Abstract

Robotic Gripper Design

Explore similar work

YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale

House of Dextra: Cross-embodied Co-design for Dexterous Hands

AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection