RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks
Summary: arXiv:2603.11558v3 Announce Type: replace-cross
Recent advancements in Vision-Language-Action (VLA) systems have revealed their potential for language-driven robotic manipulation. Nevertheless, the scaling of these systems to accommodate long-horizon tasks has presented significant challenges. Traditional pipelines often segregate data collection, policy learning, and deployment processes, leading to a heavy dependence on manual environment resets and fragile multi-policy executions. To address these challenges, researchers have introduced RoboClaw, an innovative robotics framework that integrates data collection, policy learning, and task execution within a unified VLM-driven controller.
Key Features of RoboClaw
RoboClaw offers several novel features that significantly enhance the efficiency and effectiveness of robotic manipulation tasks:
- Entangled Action Pairs (EAP): This innovative approach couples forward manipulation behaviors with inverse recovery actions, thereby creating self-resetting loops that facilitate autonomous data collection.
- Continuous On-Policy Data Acquisition: The EAP mechanism allows for ongoing data gathering and iterative policy refinement with minimal human oversight, streamlining the learning process.
- High-Level Reasoning: During deployment, RoboClaw utilizes the same agent to perform high-level reasoning, dynamically orchestrating learned policy primitives to effectively tackle long-horizon tasks.
- Consistent Contextual Semantics: By maintaining coherent contextual semantics across data collection and execution phases, RoboClaw mitigates mismatches between these stages, thereby enhancing multi-policy robustness.
Experimental Results
Extensive experiments conducted in real-world manipulation scenarios underscore the advantages of RoboClaw over conventional open-loop pipelines. The results reveal:
- A 25% improvement in success rate compared to baseline methods when tackling long-horizon tasks.
- A remarkable 53.7% reduction in human time investment throughout the robot’s lifecycle, showcasing RoboClaw’s efficiency.
Conclusion
RoboClaw represents a significant advancement in the field of robotics, particularly for tasks requiring long-term planning and execution. By unifying the processes of data collection, policy learning, and execution, this framework not only enhances the stability and scalability of robotic operations but also significantly reduces the human effort involved. As the robotics landscape continues to evolve, innovations like RoboClaw pave the way for more autonomous and efficient robotic systems capable of sophisticated manipulation tasks.
For further details, the full research can be accessed at arXiv:2603.11558v3.