AffectiveVLA: Safe and Intent-Aware VLA Control for Human-Humanoid Social Interaction
Overview
This paper introduces AffectiveVLA, a Vision-Language-Action pipeline designed for safe social interaction between humanoids and humans. The system uses onboard cameras to infer interaction intent from RGB data, converting it into language prompts for the VLA policy. It features low-confidence suppression to prevent unintended motion execution and includes compliance profiles that adjust arm impedance during contact, alongside a voice-triggered safe-stop mechanism. The authors collected approximately 1,300 multimodal episodes via VR teleoperation to fine-tune four models: GR00T, RLDX-1, Being-H0.5, and UnifoLM-VLA-0. Evaluation on the Unitree G1 identified GR00T as the best standalone model, with intent recognition integration raising its macro-F1 score from 0.75 to 0.90 and achieving a p95 inference latency of 6.27 ms for the intent model.
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Event evidence and corrections
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Reported quantity · interaction episodes: 1300 other · reported trials
“ntent from two onboard cameras and converts it into a language prompt for the VLA policy. Low-confidence predictions suppress motion execution, reducing unintended activations. During physical contact, interaction-specific compliance profiles adjust arm impedance, while an independent voice-triggered mechanism initiates a latched safe-stop procedure. Using VR teleoperation, we collected approximately 1,300 multimodal”
Exact source · revision 1Source owner not reported
Reported quantity · inference latency (p95): 6.27 other · Basis not reported
“interaction episodes and fine-tuned four VLA models: GR00T, RLDX-1, Being-H0.5, and UnifoLM-VLA-0. On-robot evaluation identifies GR00T as the best-performing standalone model. Integrating intent recognition increases its macro-F1 score from 0.75 to 0.90, while the intent model achieves a p95 inference latency of 6.27 ms. These results demonstrate that explicit intent gating improves the reliability of VLA-based soc”
Exact source · revision 1Source owner not reported
Reported quantity · training data size: 1300 other · reported trials
“ntent from two onboard cameras and converts it into a language prompt for the VLA policy. Low-confidence predictions suppress motion execution, reducing unintended activations. During physical contact, interaction-specific compliance profiles adjust arm impedance, while an independent voice-triggered mechanism initiates a latched safe-stop procedure. Using VR teleoperation, we collected approximately 1,300 multimodal”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalAffectiveVLA: Safe and Intent-Aware VLA Control for Human-Humanoid Social Interaction
This paper introduces AffectiveVLA, a Vision-Language-Action pipeline designed for safe social interaction between humanoids and humans. The system uses onboard cameras to infer interaction intent from RGB data, converting it into language prompts for the VLA policy. It features low-confidence suppression to prevent unintended motion execution and includes compliance profiles that adjust arm impedance during contact, alongside a voice-triggered safe-stop mechanism. The authors collected approximately 1,300 multimodal episodes via VR teleoperation to fine-tune four models: GR00T, RLDX-1, Being-H0.5, and UnifoLM-VLA-0. Evaluation on the Unitree G1 identified GR00T as the best standalone model, with intent recognition integration raising its macro-F1 score from 0.75 to 0.90 and achieving a p95 inference latency of 6.27 ms for the intent model.
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