ForeRobo: Unlocking Infinite Simulation Data for 3D Goal-driven Robotic Manipulation

2025-11-06

AI Summary

Addressing the challenges of sim-to-real transfer and poor skill generalization in 3D goal-driven robotic manipulation, this paper proposes ForeRobo—a closed-loop framework integrating “propose-generate-learn-execute” stages, and the first to deeply unify generative modeling with classical control. Its core contributions are: (1) ForeGen, an environment that automatically synthesizes diverse target states; and (2) ForeFormer, a transformer-based model enabling interpretable, point-wise 3D point cloud mapping and task instruction encoding. Evaluated on rigid-body and articulated-object manipulation tasks, ForeRobo achieves a 56.32% average improvement in state generation accuracy. Moreover, it demonstrates zero-shot sim-to-real transfer across over 20 real-world manipulation tasks, attaining a mean success rate of 79.28%.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Efficiently leveraging simulation to acquire advanced manipulation skills is both challenging and highly significant. We introduce extit{ForeRobo}, a generative robotic agent that utilizes generative simulations to autonomously acquire manipulation skills driven by envisioned goal states. Instead of directly learning low-level policies, we advocate integrating generative paradigms with classical control. Our approach equips a robotic agent with a self-guided extit{propose-generate-learn-actuate} cycle. The agent first proposes the skills to be acquired and constructs the corresponding simulation environments; it then configures objects into appropriate arrangements to generate skill-consistent goal states ( extit{ForeGen}). Subsequently, the virtually infinite data produced by ForeGen are used to train the proposed state generation model ( extit{ForeFormer}), which establishes point-wise correspondences by predicting the 3D goal position of every point in the current state, based on the scene state and task instructions. Finally, classical control algorithms are employed to drive the robot in real-world environments to execute actions based on the envisioned goal states. Compared with end-to-end policy learning methods, ForeFormer offers superior interpretability and execution efficiency. We train and benchmark ForeFormer across a variety of rigid-body and articulated-object manipulation tasks, and observe an average improvement of 56.32% over the state-of-the-art state generation models, demonstrating strong generality across different manipulation patterns. Moreover, in real-world evaluations involving more than 20 robotic tasks, ForeRobo achieves zero-shot sim-to-real transfer and exhibits remarkable generalization capabilities, attaining an average success rate of 79.28%.

Problem

• Developing robotic agents that autonomously acquire manipulation skills through generative simulations
• Creating infinite simulation data for 3D goal-driven robotic manipulation tasks
• Enabling zero-shot sim-to-real transfer with interpretable goal state prediction

Innovation

• Generative simulation autonomously acquires robotic manipulation skills
• ForeFormer model predicts 3D goal positions with point-wise correspondences
• Combines generative paradigms with classical control for real-world execution