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Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Jiafei Duan, Wentao Yuan, Wilbert Pumacay, Yi Ru Wang, Kiana Ehsani, Dieter Fox, Ranjay Krishna

arXiv:2406.18915v3cs.ROcs.CV

TL;DR

Robot demonstrations remain difficult to collect at sufficient quantity, quality, and diversity, while VLM-based methods have real-world, skill-design, or object-instance constraints. MANIPULATE-ANYTHING uses VLMs to identify task-relevant objects and decompose instructions, then generates actions and verifies sub-task completion with recovery attempts. Across 14 RLBench simulation tasks, MANIPULATE-ANYTHING significantly outperforms VoxPoser in 10/14; with RVT-2, policies trained on its data outperform human hand-scripted demonstrations on 5 out of 12 tasks.

  • Problem

    Robot demonstrations remain difficult to collect at sufficient quantity, quality, and diversity, while VLM-based methods have real-world, skill-design, or object-instance constraints.

    Source: §1 · pp. 1–2

  • Method

    MANIPULATE-ANYTHING uses VLMs to identify task-relevant objects and decompose instructions, then generates actions and verifies sub-task completion with recovery attempts.

    Source: §1 · p. 2, §3.1 · p. 4

  • Results

    Across 14 RLBench simulation tasks, MANIPULATE-ANYTHING significantly outperforms VoxPoser in 10/14; with RVT-2, policies trained on its data outperform human hand-scripted demonstrations on 5 out of 12 tasks.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The authors conclude that MANIPULATE-ANYTHING offers a scalable, environment-agnostic approach for generating zero-shot robot demonstrations and behavior-cloning data.

    Source: §5 · p. 10

    The system struggles with dynamic and non-prehensile tasks, depends on large language models, and still requires manual prompt engineering.

    Source: §5 · pp. 9–10

Abstract

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Large-scale endeavors like and widespread community efforts such as Open-X-Embodiment have contributed to growing the scale of robot demonstration data. However, there is still an opportunity to improve the quality, quantity, and diversity of robot demonstration data. Although vision-language models have been shown to automatically generate demonstration data, their utility has been limited to environments with privileged state information, they require hand-designed skills, and are limited to interactions with few object instances. We propose Manipulate-Anything, a scalable automated generation method for real-world robotic manipulation. Unlike prior work, our method can operate in real-world environments without any privileged state information, hand-designed skills, and can manipulate any static object. We evaluate our method using two setups. First, Manipulate-Anything successfully generates trajectories for all 7 real-world and 14 simulation tasks, significantly outperforming existing methods like VoxPoser. Second, Manipulate-Anything's demonstrations can train more robust behavior cloning policies than training with human demonstrations, or from data generated by VoxPoser, Scaling-up, and Code-As-Policies. We believe Manipulate-Anything can be a scalable method for both generating data for robotics and solving novel tasks in a zero-shot setting. Project page: https://robot-ma.github.io/.

1 Introduction

Robot demonstration data remains difficult to collect at sufficient scale and diversity, while existing VLM-driven methods face real-world and object-scope constraints. MANIPULATE-ANYTHING generates demonstrations for diverse tasks and objects, with evaluation showing zero-shot performance and utility for behavior-cloning training.

  • Human demonstration collection is expensive and does not scale to the quantity or diversity needed for robotics.Low-cost collection hardware exists, but the procedures still require expensive human data collection.Source: §1 · p. 1
  • Prior VLM systems underperform in the real world or rely on privileged state, hand-designed skills, or fixed known object instances.Some approaches succeed in simulation but underperform in real-world settings.Source: §1 · p. 1
  • MANIPULATE-ANYTHING uses VLMs to plan sub-goals, generate actions, verify progress, and re-plan after failures without privileged state or hand-designed skills.The method incorporates multi-viewpoint reasoning to improve performance and adds recovery behavior to collected demonstrations.Source: §1 · p. 2
  • In zero-shot evaluation across 14 RLBench simulation tasks, MANIPULATE-ANYTHING significantly outperforms VoxPoser on 10/14, and across real-world tasks achieves a task-averaged success rate of 38.57%.Policies trained on MANIPULATE-ANYTHING-generated data outperform hand-scripted demonstrations on 5/12 tasks and perform on par on 4 more when evaluated with RVT-2.Source: §1 · p. 2

2 Related work

Prior work scales manipulation data through human annotation, simulation, exploration, learned policies, or planning, while VLMs have been applied to language planning and program generation. MANIPULATE-ANYTHING combines language-model planning and vision-based scene understanding to synthesize trajectories in simulated and real environments.

Scaling manipulation data

  • Prior data-scaling approaches include human action annotations, simulated language annotation, random exploration, learned policies, egocentric videos, and task-and-motion planning.Human annotation is limited in scale, while egocentric video lacks action labels and requires cross-embodiment transfer.Source: §2 · p. 3
  • MANIPULATE-ANYTHING combines VLM scene understanding, LLM common-sense knowledge, object-pose prediction, and dynamic retry to synthesize demonstrations in simulated and real environments.The framework passage depicts a scene and instruction entering a VLM, followed by action generation, sub-task verification, and filtering for successful demonstrations.Source: §2 · p. 3, Figure 2 · p. 3

Language models for robotics

  • Language models have been used for policy learning, planning, summarization, program synthesis, optimization, and physical grounding in robotics.Related work also integrates language models with task-and-motion planners to create expert demonstrations.Source: §2 · p. 3

3 MANIPULATE-ANYTHING

MANIPULATE-ANYTHING turns free-form language instructions into verified robot actions through task decomposition, viewpoint selection, and action generation. Its framework supports both newly generated skills and task-specific grasp poses.

3 MANIPULATE-ANYTHING

  • The framework decomposes language-conditioned everyday manipulation tasks into sub-tasks, generates code or task-specific grasp poses, and verifies sub-task success.The framework is modular and is described as continuing to improve as its underlying VLMs improve.Source: §3 · pp. 3–4

3.1 Task plan generation

  • A free-form instruction T is converted into ordered sub-tasks Ti paired with verification conditions vi, which guide action generation and recovery attempts.The method identifies task-relevant objects before decomposing the instruction.Source: §3.1 · p. 4

3.2 Multi-viewpoint VLM selection

  • The VLM selects a task-conditioned viewpoint from multiple camera or re-rendered views for action generation or sub-task verification.The views are concatenated into a single frame, with labels identifying each viewpoint.Source: §3.2 · p. 4

3.3 Action generation module

  • For agent-centric actions, the VLM grounds code-generated motions in object locations and scene context using selected viewpoints.For object-centric actions, object-agnostic candidate grasps are filtered using task-conditioned object-part boxes, then the highest-confidence pose is selected.Source: §3.3 · pp. 4–5

3.4 Sub-task verification

  • MANIPULATE-ANYTHING, an open-vocabulary autonomous robot demonstration generation system, shows zero-shot demonstrations for 14 tasks in simulation and 7 tasks in the real world, and verifies each sub-task’s success.Source: §3 · p. 4, Figure 3 · p. 5

4 Experiments

The experiments test zero-shot task completion and whether generated demonstrations can train robust behavior-cloning policies. MANIPULATE-ANYTHING covers all 14 simulation tasks, performs on par with human demonstrations in simulation, and generates successful demonstrations for all seven real-world tasks.

4.1 Zero-shot Performance in Simulation

  • MANIPULATE-ANYTHING generates successful trajectories for all 14 simulation tasks and outperforms the baselines in 10, despite their access to privileged state information.Scaling-up, VoxPoser, and CAP cover 10, 9, and 7 tasks, respectively; MANIPULATE-ANYTHING exceeds VoxPoser by an average task-averaged margin of up to 22%.Source: Table 1 · p. 6, §4.1 · p. 6

4.2 Behavior cloning with demonstrations from MANIPULATE-ANYTHING

  • MANIPULATE-ANYTHING data yields PerAct performance similar to hand-scripted demonstrations (p = 0.973) and the best behavior-cloning performance among autonomous baselines on 10 of 12 tasks.The difference from hand-scripted demonstrations is 0.27% across tasks; policies trained on VoxPoser, Scaling-up, and CAP data perform statistically worse (p ≤0.01).Source: §4.2 · p. 7, Table 2 · p. 7

4.3 Real-world experiments

  • MANIPULATE-ANYTHING generates successful demonstrations for all seven real-world tasks, exceeds CAP by 38%, and achieves over 25% success even on its worst-performing task.The resulting trained policies outperform zero-shot performance on four of five tasks; the sort_object policy underperforms because the task requires longer-horizon memory.Source: §4.3 · p. 8
  • Real-world policies trained on MANIPULATE-ANYTHING data perform on par with policies trained on human-expert data, while outperforming zero-shot performance on four of five tasks.Table 3 compares success rates for zero-shot Code as Policies and MANIPULATE-ANYTHING against policies trained on MANIPULATE-ANYTHING and human-expert data.Source: §4.3 · p. 8, Table 3 · p. 9

4.4 Ablations

  • More MANIPULATE-ANYTHING demonstrations improve PerAct performance, with a linear-fit slope of 0.503 versus 0.197 for RLBench-generated data; it also outperforms VoxPoser across language and object variations.The scaling comparison uses 1 to 100 training demonstrations for put_block; the generalization experiment varies instructions and target objects for sort_mustard.Source: Figure 4 · p. 8, §4.4 · p. 9

4.5 Error breakdown

  • The error study separates VLM perception errors in object detection and viewpoint selection from verification-related reasoning errors, using human decisions as replacements on play_jenga.The study is limited to play_jenga because of resource constraints.Source: §4.5 · p. 9, Figure 6 · p. 9

5 Discussion

MANIPULATE-ANYTHING has practical boundaries, including reliance on large language models, difficulty with dynamic and non-prehensile tasks, and a continuing need for manual prompt engineering. The paper concludes that its environment-agnostic, error-recovering system can generate behavior-cloning data that achieves better performance than human data.

Limitations

  • The system struggles with dynamic and non-prehensile tasks because it cannot generate alternative grasp poses for these scenarios.Source: §5 · p. 9
  • Its modular integration of multiple VLMs can lead to compounding errors when generating zero-shot trajectories.Source: §5 · p. 9
  • The method depends on large language models, although the authors expect ongoing open-source LLM development to diminish this reliance over time.Source: §5 · p. 9
  • Manual prompt engineering for in-context learning is still required, though alignment and prompting advances may reduce the effort.Source: §5 · p. 10

Conclusion

  • The conclusion describes an environment-agnostic approach that uses LLMs for high-level planning and scene understanding, supports error recovery, and generates behavior-cloning data that achieves better performance than human data.Source: §5 · p. 10
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