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RoboCAS: A Benchmark for Robotic Manipulation in Complex Object Arrangement Scenarios

Liming Zheng, Feng Yan, Fanfan Liu, Chengjian Feng, Zhuoliang Kang, Lin Ma

TL;DR

RoboCAS introduces a first benchmark focused on long-horizon robotic manipulation in complex object arrangements, addressing clutter, occlusion, and inter-object interference under language instructions. Built in a realistic SAPIEN-based simulation with real-object scans, it enables automated scripted data generation across scattered, orderly, and stacked layouts for picking, selecting, and searching tasks. Experimental results with RT-1 and RoboFlamingo show meaningful success in simple layouts but substantial gaps in stacked, cluttered scenarios, underscoring the need for advanced spatial reasoning and chain-reaction understanding. The benchmark provides a cost-effective platform to drive progress in embodied AI toward robust, real-world manipulation under ambiguous language and complex environments.

Abstract

Foundation models hold significant potential for enabling robots to perform long-horizon general manipulation tasks. However, the simplicity of tasks and the uniformity of environments in existing benchmarks restrict their effective deployment in complex scenarios. To address this limitation, this paper introduces the \textit{RoboCAS} benchmark, the first benchmark specifically designed for complex object arrangement scenarios in robotic manipulation. This benchmark employs flexible and concise scripted policies to efficiently collect a diverse array of demonstrations, showcasing scattered, orderly, and stacked object arrangements within a highly realistic physical simulation environment. It includes complex processes such as target retrieval, obstacle clearance, and robot manipulation, testing agents' abilities to perform long-horizon planning for spatial reasoning and predicting chain reactions under ambiguous instructions. Extensive experiments on multiple baseline models reveal their limitations in managing complex object arrangement scenarios, underscoring the urgent need for intelligent agents capable of performing long-horizon operations in practical deployments and providing valuable insights for future research directions. Project website: \url{https://github.com/notFoundThisPerson/RoboCAS-v0}.

RoboCAS: A Benchmark for Robotic Manipulation in Complex Object Arrangement Scenarios

TL;DR

RoboCAS introduces a first benchmark focused on long-horizon robotic manipulation in complex object arrangements, addressing clutter, occlusion, and inter-object interference under language instructions. Built in a realistic SAPIEN-based simulation with real-object scans, it enables automated scripted data generation across scattered, orderly, and stacked layouts for picking, selecting, and searching tasks. Experimental results with RT-1 and RoboFlamingo show meaningful success in simple layouts but substantial gaps in stacked, cluttered scenarios, underscoring the need for advanced spatial reasoning and chain-reaction understanding. The benchmark provides a cost-effective platform to drive progress in embodied AI toward robust, real-world manipulation under ambiguous language and complex environments.

Abstract

Foundation models hold significant potential for enabling robots to perform long-horizon general manipulation tasks. However, the simplicity of tasks and the uniformity of environments in existing benchmarks restrict their effective deployment in complex scenarios. To address this limitation, this paper introduces the \textit{RoboCAS} benchmark, the first benchmark specifically designed for complex object arrangement scenarios in robotic manipulation. This benchmark employs flexible and concise scripted policies to efficiently collect a diverse array of demonstrations, showcasing scattered, orderly, and stacked object arrangements within a highly realistic physical simulation environment. It includes complex processes such as target retrieval, obstacle clearance, and robot manipulation, testing agents' abilities to perform long-horizon planning for spatial reasoning and predicting chain reactions under ambiguous instructions. Extensive experiments on multiple baseline models reveal their limitations in managing complex object arrangement scenarios, underscoring the urgent need for intelligent agents capable of performing long-horizon operations in practical deployments and providing valuable insights for future research directions. Project website: \url{https://github.com/notFoundThisPerson/RoboCAS-v0}.
Paper Structure (35 sections, 3 equations, 5 figures, 3 tables)

This paper contains 35 sections, 3 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Language-conditioned robot learning
  4. Datasets and benchmarks for robot manipulation
  5. The RoboCAS benchmark
  6. Simulation environment
  7. Data collection
  8. Scene configuration
  9. Task design
  10. Picking task.
  11. Selecting task.
  12. Searching task.
  13. Trajectory generation
  14. Experiments
  15. Experimental setup
  16. ...and 20 more sections

Figures (5)

  • Figure 1: Visualization. (a) Real-world datasets: The amount of data collected on real robots is relatively small, and the scene layouts are quite simple. (b) Simulation datasets: Although there is a large volume of data, the environments are monotonous and the tasks are simple. (c) Real-world scenes: The complexity of object placement far exceeds that in (a) and (b).
  • Figure 2: Environment setups of RoboCAS. (a) Our environment can provide numerous of scenes by just editing several parameters in configuration files. (b) Three types of scene layouts for manipulable objects are supported: scattered, orderly, and stacked. Due to variations in distances between objects, different layouts present distinct grasping methods and challenges.
  • Figure 3: The three types of tasks supported in the RoboCAS benchmark. Picking: Pick up the specified target and move it to the designated location. Selecting: Choose and grasp a specific target from multiple identically arranged targets. Searching: Find a partially obscured specific target in a stacked scene, clear any obstacles, and then grasp it.
  • Figure 4: The generation process of our RoboCAS benchmark: (a) Target Selection: After initializing the environment based on the scene template, a target is randomly selected from objects that meet the task's requirements for target state. (b) Grasp Pose Sampling: After filtering through collision detection and kinematic calculations of the agent, an appropriate grasp pose is selected from the force-closure annotations. (c) Obstacle Removal: Obstacles that may hinder the operation of the target are removed using methods such as pushing or flicking. (d) Path Planning: The agent uses the RRT-Connect algorithm to plan a collision-free path to the specified end effector (EEF) pose, integrating collision information provided by the simulator.
  • Figure 5: Failure cases occurred in our experiments. (a) Infeasible grasp pose that lead to the slipping of the target object. (b) Failure cause by the collision between target and agent and the corresponding pose change.