A Benchmark of Dexterity for Anthropomorphic Robotic Hands

• URL: http://arxiv.org/abs/2604.09294v1
• Date: Fri, 10 Apr 2026 13:04:28 GMT
• Title: A Benchmark of Dexterity for Anthropomorphic Robotic Hands
• Abstract summary: Dexterity is a central yet ambiguously defined concept in the design and evaluation of anthropomorphic robotic hands.<n>We introduce POMDAR, a comprehensive benchmark that formalizes dexterity as task performance across a structured set of manipulation and grasping motions.
• Score: 6.846001511743122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dexterity is a central yet ambiguously defined concept in the design and evaluation of anthropomorphic robotic hands. In practice, the term is often used inconsistently, with different systems evaluated under disparate criteria, making meaningful comparisons across designs difficult. This highlights the need for a unified, performance-based definition of dexterity grounded in measurable outcomes rather than proxy metrics. In this work, we introduce POMDAR, a comprehensive dexterity benchmark that formalizes dexterity as task performance across a structured set of manipulation and grasping motions. The benchmark was systematically derived from established taxonomies in human motor control. It is implemented in both real-world and simulation and includes four manipulation configurations: vertical and horizontal configurations, continuous rotation, and pure grasping. The task designs contain mechanical scaffolding to constrain task motion, suppress compensatory strategies, and enable metrics to be measured unambiguously. We define a quantitative scoring metric combining task correctness and execution speed, effectively measuring dexterity as throughput. This enables objective, reproducible, and interpretable evaluation across different hand designs. POMDAR provides an open-source, standardized, and taxonomy-grounded benchmark for consistent comparison and evaluation of anthropomorphic robot hands to facilitate a systematic advancement of dexterous manipulation platforms. CAD, simulation files, and evaluation videos are publicly available at https://srl-ethz.github.io/POMDAR/.

Related papers

• IMBench: A Benchmark for Intuitive Robotic Manipulation [11.843368723732679]
  Humans combine reasoning and motor control to solve complex manipulation tasks under diverse constraints.<n>Existing benchmarks fail to capture this integration.<n>We introduce IMBENCH, a benchmark designed to evaluate intuitive manipulation as an integrated capability spanning perception, physical reasoning, action generation, and iterative execution.
  arXiv  Detail & Related papers  (2026-07-17T05:34:29Z)
• DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo [82.73355932907565]
  DexJoCo is a benchmark and toolkit for task-oriented dexterous manipulation.<n>We present 11 functionally grounded tasks that evaluate tool-use, bimanual coordination, long-horizon execution, and reasoning.<n>We benchmark modern models under diverse settings, including visual and dynamics randomization, multi-task training, and action-head adaptation.
  arXiv  Detail & Related papers  (2026-05-15T17:59:51Z)
• Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons [69.87766750714945]
  General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations.<n>We introduce Robometer, a scalable reward modeling framework that combines intra-trajectory progress supervision with inter-trajectory preference supervision.<n>Robometer is trained with a dual objective: a frame-level progress loss that anchors reward magnitude on expert data, and a trajectory-comparison preference loss that imposes global ordering constraints.
  arXiv  Detail & Related papers  (2026-03-02T17:38:58Z)
• AmbiBench: Benchmarking Mobile GUI Agents Beyond One-Shot Instructions in the Wild [30.138230316314534]
  We introduce AmbiBench, the first benchmark incorporating a taxonomy of instruction clarity to shift evaluation from unidirectional instruction to bidirectional intent alignment.<n>We construct a rigorous dataset of 240 ecologically valid tasks across 25 applications, subject to strict review protocols.<n>We also develop MUSE, an automated framework utilizing an MLLM-as-a-judge multi-agent architecture.
  arXiv  Detail & Related papers  (2026-02-12T09:25:15Z)
• Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization [65.37179698521766]
  Vision-Language-Action (VLA) models show strong promise for generalist robot control.<n>It remains unclear whether -- and under what conditions -- the standard "scale data" recipe translates to robotics.<n>We present a systematic, controlled study of VLA scaling that revisits core training choices for pretraining across diverse robots.
  arXiv  Detail & Related papers  (2026-02-10T12:25:43Z)
• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• The Geometry of Benchmarks: A New Path Toward AGI [0.0]
  We introduce a geometric framework in which all psychometric batteries for AI agents are treated as points in a structured moduli space.<n>First, we define an Autonomous AI (AAI) Scale, a Kardashev-style hierarchy of autonomy grounded in measurable performance.<n>Second, we construct a moduli space of batteries, identifying equivalence classes of benchmarks that are indistinguishable at the level of agent orderings and capability inferences.<n>Third, we introduce a general Generator-Verifier-Updater (GVU) operator that subsumes reinforcement learning, self-play, debate and verifier-based fine-tuning
  arXiv  Detail & Related papers  (2025-12-03T21:34:09Z)
• RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation [32.080769025457926]
  We present RoboEval, a simulation benchmark and structured evaluation framework designed to reveal the limitations of current bimanual manipulation policies.<n>RoboEval introduces a suite of tiered, semantically grounded tasks that systematically challenge spatial, physical, and coordination capabilities.<n>We find that behavioral metrics correlate with success in over half of task-metric pairs, and remain informative even when binary success saturates.
  arXiv  Detail & Related papers  (2025-07-01T05:33:16Z)
• Statistical Uncertainty Quantification for Aggregate Performance Metrics in Machine Learning Benchmarks [0.0]
  We show how statistical methodology can be used for quantifying uncertainty in metrics that have been aggregated across multiple tasks.<n>These techniques reveal insights such as the dominance of a specific model for certain types of tasks despite an overall poor performance.
  arXiv  Detail & Related papers  (2025-01-08T02:17:34Z)
• Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications [3.1158660854608824]
  Continuous Goal-Directed Actions (CGDA) encodes actions as changes of any feature that can be extracted from the environment. Current strategies involve performing evaluations in a simulation, transferring the final joint trajectory to the actual robot. Two different approaches to reduce the number of evaluations using EA, are proposed and compared.
  arXiv  Detail & Related papers  (2024-02-01T15:38:21Z)
• A Control-Centric Benchmark for Video Prediction [69.22614362800692]
  We propose a benchmark for action-conditioned video prediction in the form of a control benchmark. Our benchmark includes simulated environments with 11 task categories and 310 task instance definitions. We then leverage our benchmark to study the effects of scaling model size, quantity of training data, and model ensembling.
  arXiv  Detail & Related papers  (2023-04-26T17:59:45Z)
• ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning [63.77667876176978]
  Large language models show improved downstream task interpretability when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness is difficult. We present ROS, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics.
  arXiv  Detail & Related papers  (2022-12-15T15:52:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.