ROG-Grasp estimates produce orientation from root surface geometry via YOLO detection and point cloud plane fitting to generate stable grasp poses and constrained motion plans, achieving higher reliability and speed than VLA policies in tomato and onion experiments.
arXiv preprint arXiv:2509.14143 (2025)
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abstract
Vision-language-action (VLA) models have recently emerged as a promising paradigm for robotic control, enabling end-to-end policies that ground natural language instructions into visuomotor actions. However, current VLAs often struggle to satisfy precise task constraints, such as stopping based on numeric thresholds, since their observation-to-action mappings are implicitly shaped by training data and lack explicit mechanisms for condition monitoring. In this work, we propose CLAW (CLIP-Language-Action for Weight), a framework that decouples condition evaluation from action generation. CLAW leverages a fine-tuned CLIP model as a lightweight prompt generator, which continuously monitors the digital readout of a scale and produces discrete directives based on task-specific weight thresholds. These prompts are then consumed by $\pi_0$, a flow-based VLA policy, which integrates the prompts with multi-view camera observations to produce continuous robot actions. This design enables CLAW to combine symbolic weight reasoning with high-frequency visuomotor control. We validate CLAW on three experimental setups: single-object grasping and mixed-object tasks requiring dual-arm manipulation. Across all conditions, CLAW reliably executes weight-aware behaviors and outperforms both raw-$\pi_0$ and fine-tuned $\pi_0$ models. A video of our paper is available online https://youtu.be/MuMYj2QgReI.
fields
cs.RO 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
VILAS integrates low-cost modular hardware with a kirigami soft gripper and evaluates fine-tuned pi_0, pi_0.5, and GR00T N1.6 models on grape grasping using a ZMQ-based teleoperation and deployment framework.
Threading optimization of RTAC for VLA models reduces end-to-end latency and improves stability on low-cost agricultural robotic arms without changing the policy.
citing papers explorer
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ROG-Grasp: Root-Oriented Geometry for Robotic Grasping and Placement
ROG-Grasp estimates produce orientation from root surface geometry via YOLO detection and point cloud plane fitting to generate stable grasp poses and constrained motion plans, achieving higher reliability and speed than VLA policies in tomato and onion experiments.
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VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation
VILAS integrates low-cost modular hardware with a kirigami soft gripper and evaluates fine-tuned pi_0, pi_0.5, and GR00T N1.6 models on grape grasping using a ZMQ-based teleoperation and deployment framework.
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Threading Optimization for Vision-Language-Action Model Inference in Low-Cost Smart Agricultural Manipulation
Threading optimization of RTAC for VLA models reduces end-to-end latency and improves stability on low-cost agricultural robotic arms without changing the policy.