arXiv:2605.01507v1 Announce Type: new
Abstract: Prior studies report that partial driving automation can increase the cognitive demands on human drivers. This effect largely arises from human drivers’ lack of transparent insight into the vehicle’s int…
arXiv:2508.14994v3 Announce Type: replace-cross
Abstract: In hazardous and remote environments, robotic systems perform critical tasks demanding improved safety and efficiency. Among these, quadruped robots with manipulator arms offer mobility and ver…
arXiv:2603.07866v2 Announce Type: replace
Abstract: Robust grasping in cluttered, unstructured environments remains challenging for mobile legged manipulators due to occlusions that lead to partial observations, unreliable depth estimates, and the nee…
arXiv:2605.02918v1 Announce Type: cross
Abstract: Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are often chosen to minimize…
arXiv:2604.10647v3 Announce Type: replace
Abstract: UMI-style interfaces enable scalable robot learning, but existing systems remain largely visuomotor, relying primarily on RGB observations and trajectory while providing only limited access to physic…
arXiv:2605.01566v1 Announce Type: new
Abstract: Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw performance over cost-effective compute usage. Howe…
arXiv:2605.03302v1 Announce Type: new
Abstract: This paper mainly studies the accurate height jumping control of wheeled-bipedal robots based on torque planning and energy consumption optimization. Due to the characteristics of underactuated, nonlinea…
arXiv:2605.03360v1 Announce Type: cross
Abstract: We present A-CODE, a fully atomic unified one-stage protein co-design model that simultaneously refines discrete atom types and continuous atom coordinates. Unlike predominant two-stage methods that ca…
arXiv:2605.03578v1 Announce Type: cross
Abstract: Boltzmann Machines trained on evolutionary sequence data have emerged as a powerful paradigm for the data-driven design of artificial proteins. However, the relationship between model architecture, spe…
arXiv:2512.22608v3 Announce Type: replace
Abstract: Due to the high value and high failure rates of startups, predicting their success is a critical challenge. Existing approaches typically model startup success from a single decision-maker’s perspect…