RoboPlayground: Democratizing Robotic Evaluation through Structured Physical Domains

• URL: http://arxiv.org/abs/2604.05226v1
• Date: Mon, 06 Apr 2026 22:42:05 GMT
• Title: RoboPlayground: Democratizing Robotic Evaluation through Structured Physical Domains
• Abstract summary: Evaluation of robotic manipulation systems has largely relied on fixed benchmarks authored by experts.<n>We argue that evaluating modern manipulation policies requires reframing evaluation as a language-driven process.<n>We present RoboPlayground, a framework that enables users to author executable manipulation tasks using natural language.
• Score: 19.460905215536254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluation of robotic manipulation systems has largely relied on fixed benchmarks authored by a small number of experts, where task instances, constraints, and success criteria are predefined and difficult to extend. This paradigm limits who can shape evaluation and obscures how policies respond to user-authored variations in task intent, constraints, and notions of success. We argue that evaluating modern manipulation policies requires reframing evaluation as a language-driven process over structured physical domains. We present RoboPlayground, a framework that enables users to author executable manipulation tasks using natural language within a structured physical domain. Natural language instructions are compiled into reproducible task specifications with explicit asset definitions, initialization distributions, and success predicates. Each instruction defines a structured family of related tasks, enabling controlled semantic and behavioral variation while preserving executability and comparability. We instantiate RoboPlayground in a structured block manipulation domain and evaluate it along three axes. A user study shows that the language-driven interface is easier to use and imposes lower cognitive workload than programming-based and code-assist baselines. Evaluating learned policies on language-defined task families reveals generalization failures that are not apparent under fixed benchmark evaluations. Finally, we show that task diversity scales with contributor diversity rather than task count alone, enabling evaluation spaces to grow continuously through crowd-authored contributions. Project Page: https://roboplayground.github.io

Related papers

• A Brain-inspired Hierarchical Framework for Zero-Shot Robot Task Reasoning and Execution [9.745144598922584]
  Method is a zero-shot hierarchical framework inspired by the division of roles in the human brain.<n>It grounds commands in explicit object states, composes reusable atomic actions into task-conditioned sequences, and ranks alternative sequences by execution cost.<n>Method achieves higher success rates than ReKep, Dream2Flow, and $_0.5$ benchmarks.
  arXiv  Detail & Related papers  (2026-09-05T08:59:04Z)
• One Word, Different Action: A Real-Robot Benchmark for Language-Conditioned Embodied Reasoning [5.503904431371255]
  We introduce One Word, Different Action, a real-robot benchmark built on physical decision states and executable actions.<n>Experiments show that modern models are near saturation on single-constraint instruction changes, yet several models degrade noticeably when multiple task constraints must be integrated into one executable decision.
  arXiv  Detail & Related papers  (2026-09-04T15:21:58Z)
• 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)
• 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)
• CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity [23.77040677368575]
  We introduce a novel robotic manipulation framework that can accomplish tasks specified by potentially ambiguous natural language.<n>This framework employs a Vision-Language Model (VLM) to interpret abstract concepts in natural language instructions.<n>We show that our approach excels across challenging manipulation tasks involving language ambiguity, contact-rich manipulation, and multi-object interactions.
  arXiv  Detail & Related papers  (2025-06-19T23:42:03Z)
• StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs [78.84060166851805]
  StructTest is a novel benchmark that evaluates large language models (LLMs) on their ability to follow compositional instructions and generate structured outputs.<n> Assessments are conducted deterministically using a rule-based evaluator, which can be easily extended to new tasks and datasets.<n>We demonstrate that StructTest remains challenging even for top-performing models like Deepseek-V3/R1 and GPT-4o.
  arXiv  Detail & Related papers  (2024-12-23T22:08:40Z)
• Fully Automated Task Management for Generation, Execution, and Evaluation: A Framework for Fetch-and-Carry Tasks with Natural Language Instructions in Continuous Space [1.2691047660244337]
  This paper aims to develop a framework that enables a robot to execute tasks based on visual information. We propose a framework for the full automation of the generation, execution, and evaluation of FCOG tasks. In addition, we introduce an approach to solving the FCOG tasks by dividing them into four distinct subtasks.
  arXiv  Detail & Related papers  (2023-11-07T15:38:09Z)
• Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model [63.66204449776262]
  Instruct2Act is a framework that maps multi-modal instructions to sequential actions for robotic manipulation tasks. Our approach is adjustable and flexible in accommodating various instruction modalities and input types. Our zero-shot method outperformed many state-of-the-art learning-based policies in several tasks.
  arXiv  Detail & Related papers  (2023-05-18T17:59:49Z)
• ICE-Score: Instructing Large Language Models to Evaluate Code [7.556444391696562]
  We propose textttICE-Score, a new evaluation metric via instructing large language models for code assessments. Our metric addresses the limitations of existing approaches by achieving superior correlations with functional correctness and human preferences. Our results demonstrate that our metric surpasses state-of-the-art metrics for code generation.
  arXiv  Detail & Related papers  (2023-04-27T16:38:17Z)
• Analyzing the Limits of Self-Supervision in Handling Bias in Language [52.26068057260399]
  We evaluate how well language models capture the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing. Our analyses indicate that language models are capable of performing these tasks to widely varying degrees across different bias dimensions, such as gender and political affiliation.
  arXiv  Detail & Related papers  (2021-12-16T05:36:08Z)
• Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning [69.1137074774244]
  Leveraging language interactions effectively requires addressing limitations in the two most common approaches to language grounding. We introduce the idea of neural abstructions: a set of constraints on the inference procedure of a label-conditioned generative model. We show that with this method a user population is able to build a semantic modification for an open-ended house task in Minecraft.
  arXiv  Detail & Related papers  (2021-07-20T07:01:15Z)
• A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution [54.385344986265714]
  We propose a persistent spatial semantic representation method to bridge the gap between language and robot actions. We evaluate our approach on the ALFRED benchmark and achieve state-of-the-art results, despite completely avoiding the commonly used step-by-step instructions.
  arXiv  Detail & Related papers  (2021-07-12T17:47:19Z)
• Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding [101.24748444126982]
  Decomposable tasks are complex and comprise of a hierarchy of sub-tasks. Existing benchmarks, however, typically hold out examples for only the surface-level sub-task. We propose a framework to construct robust test sets using coordinate ascent over sub-task specific utility functions.
  arXiv  Detail & Related papers  (2021-06-29T02:53:59Z)
• CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning [138.40338621974954]
  CausalWorld is a benchmark for causal structure and transfer learning in a robotic manipulation environment. Tasks consist of constructing 3D shapes from a given set of blocks - inspired by how children learn to build complex structures.
  arXiv  Detail & Related papers  (2020-10-08T23:01:13Z)

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.