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Manipulate-Anything: Automating Real-World Robots using Vision-Language Models
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Manipulate-Anything: Automating Real-World Robots using Vision-Language Models
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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/.
Forward citations
Cited by 13 Pith papers
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World Action Planner: Generalizable Decision-Making with Action-Conditioned World Models
Pairing VLM-generated action proposals with rollouts from a pose-image-conditioned video world model yields high success rates in novel simulated manipulation tasks without end-to-end policy retraining.
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RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation
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CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation
CoStream composes semantic, predictive, and reactive behaviors on an SE(3) interface to enable precise, generalizable performance on eight real-world contact-rich manipulation tasks.
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From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation
AgentChord models manipulation tasks as directed graphs enriched with anticipatory recovery branches, using specialized agents to enable immediate, low-latency failure responses and improve success on long-horizon bim...
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PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation
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Robix: A Unified Model for Robot Interaction, Reasoning and Planning
A three-stage-trained VLM unifies robot planning and dialogue, and beats commercial VLMs on the authors' interactive-task benchmarks.
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$\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
π_{0.5} is a VLA model that achieves long-horizon dexterous manipulation in entirely new homes through co-training on heterogeneous tasks and multi-source data including web and semantic predictions.
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Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
OpenVLA-OFT fine-tuning boosts LIBERO success rate from 76.5% to 97.1%, speeds action generation 26x, and outperforms baselines on real bimanual dexterous tasks.
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RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control
An architecture that mediates LLM computer-use agents for UAV control by compiling agent decisions into validated, time-bounded, evidence-logged skill invocations, with a prototype on OpenClaw/PX4/OP-TEE.
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CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation
Complex manipulation emerges from composing semantic, predictive, and reactive behaviors on a shared SE(3) interface executed by a compliant controller.
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InSight: Self-Guided Skill Acquisition via Steerable VLAs
InSight enables autonomous acquisition of manipulation primitives in VLAs via automated segmentation for steerability and a VLM-guided data flywheel that generates and integrates new demonstrations for tasks like pour...
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