Pith. sign in

REVIEW 6 cited by

Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1502.03143 v1 pith:IKC2IY7E submitted 2015-02-10 cs.RO

classification cs.RO
keywords manipulationbenchmarkingobjectsobjectresearchusedwillalong
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be used to facilitate benchmarking in robotic manipulation, prosthetic design and rehabilitation research. The objects in the set are designed to cover a wide range of aspects of the manipulation problem; it includes objects of daily life with different shapes, sizes, textures, weight and rigidity, as well as some widely used manipulation tests. The associated database provides high-resolution RGBD scans, physical properties, and geometric models of the objects for easy incorporation into manipulation and planning software platforms. In addition to describing the objects and models in the set along with how they were chosen and derived, we provide a framework and a number of example task protocols, laying out how the set can be used to quantitatively evaluate a range of manipulation approaches including planning, learning, mechanical design, control, and many others. A comprehensive literature survey on existing benchmarks and object datasets is also presented and their scope and limitations are discussed. The set will be freely distributed to research groups worldwide at a series of tutorials at robotics conferences, and will be otherwise available at a reasonable purchase cost. It is our hope that the ready availability of this set along with the ground laid in terms of protocol templates will enable the community of manipulation researchers to more easily compare approaches as well as continually evolve benchmarking tests as the field matures.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A new benchmark, GCA-Bench, evaluates robotic grasping from detection to execution across 102 complex tasks and finds current VLA and detection-based methods score below 70% success.

  2. Unified Motion-Action Modeling for Heterogeneous Robot Learning

    cs.RO 2026-06 unverdicted novelty 6.0 of 10

    UMA treats object motion and robot actions as co-evolving variables under a masked generative objective with hindsight relabeling and contrastive disentanglement to support multi-task pretraining and deployment across...

  3. Physics-informed Neural Time Fields for Prehensile Object Manipulation

    cs.RO 2025-08 conditional novelty 6.0 of 10

    POM-NeTF extends physics-informed neural time fields from robot motion planning to prehensile object manipulation, enabling fast, demonstration-free planning with re-grasping in cluttered environments.

  4. Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A hybrid 3D Gaussian splatting plus explicit mesh representation, optimized end-to-end with differentiable rendering and physics, reconstructs objects and calibrates robot poses from imperfect real-world RGB trajectories.

  5. Co-Design of Soft Gripper with Neural Physics

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A simulation-trained neural surrogate jointly optimizes stiffness distribution and grasp pose for a soft gripper, improving hardware grasp success over rigid and soft baselines.

  6. Modular Anthropomorphic Hand Design via Multi-Parameter Finger Benchmarking and Selection

    cs.RO 2026-06 unverdicted novelty 4.0 of 10

    A modular benchmarking framework optimizes finger designs (joint, bone, skin, sensors) via mechanism and task metrics, then integrates them to improve hand performance in grasping and screwing tasks.

Pith tools