Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration

Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collaborative systems rely on hand-engineered pipelines, limiting their scalability and flexibility for new tasks. In this work, we show that models trained end-to-end with imitation learning, specifically vision-language-action (VLA) models, can support collaborative manipulation, and characterize the key factors affecting their real-world performance. We evaluate two state-of-the-art models and identify a failure mode of action-chunking policies in implicit HRC, where demonstration action leakage (i.e., action chunks crossing latent task transitions) can cause premature assistive behavior. We find that this issue increases with longer execution horizons and occurs in real-world collaborative VLA systems, such as when a robot attempts to hand over a tool before the person is ready. We propose an inference-time steering method to mitigate these erroneous assistive actions while preserving policy performance. Finally, through a 16-participant user study on a long-horizon collaborative assembly task, we show that steering enables a longer execution horizon while mitigating premature assistance, leading to faster collaboration and fewer failures compared to a shorter-horizon policy.

Susceptibility of Adversarial Attack on Medical Image Segmentation Models

The nature of deep neural networks has given rise to a variety of attacks, but little work has been done to address the effect of adversarial attacks on segmentation models trained on MRI datasets. In light of the grave consequences that such attacks could cause, we explore four models from the U-Net family and examine their responses to the Fast Gradient Sign Method (FGSM) attack. We conduct FGSM attacks on each of them and experiment with various schemes to conduct the attacks. In this paper, we find that medical imaging segmentation models are indeed vulnerable to adversarial attacks and that there is a negligible correlation between parameter size and adversarial attack success. Furthermore, we show that using a different loss function than the one used for training yields higher adversarial attack success, contrary to what the FGSM authors suggested. In future efforts, we will conduct the experiments detailed in this paper with more segmentation models and different attacks. We will also attempt to find ways to counteract the attacks by using model ensembles or special data augmentations. Our code is available at https://github.com/ZhongxuanWang/adv_attk