Progress-enhanced VLA model raises simulated bimanual furniture assembly success from 48% to 80% across three furniture types and shows 16% drop on real Kinova robot.
Seqvla: Sequential task execution for long-horizon manipulation with completion-aware vision- language-action model
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
WeaveLA improves VLA policies for repetitive robot manipulation by event-triggered cross-subtask latent memory weaving, raising success on the hardest repetition tasks from 0% to 47.8% while leaving single-execution performance unchanged.
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.
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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FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model
Progress-enhanced VLA model raises simulated bimanual furniture assembly success from 48% to 80% across three furniture types and shows 16% drop on real Kinova robot.
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WeaveLA: Event Driven Cross-Subtask Latent Memory Weaving for Repetitive Robot Manipulation
WeaveLA improves VLA policies for repetitive robot manipulation by event-triggered cross-subtask latent memory weaving, raising success on the hardest repetition tasks from 0% to 47.8% while leaving single-execution performance unchanged.
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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.
- SADP: Subgoal-Aware Diffusion Policy for Long-Horizon Manipulation Learned from Foundation Model Generated Demonstrations