arXiv:2510.05213v2 Announce Type: replace-cross
Abstract: Pretrained vision foundation models (VFMs) advance robotic learning via rich visual representations, yet individual VFMs typically excel only in specific domains, limiting generality across tas…
arXiv:2605.15049v1 Announce Type: new
Abstract: This paper presents a prototyping framework for distributed control of multi-robot systems, aimed at bridging theory and practical testing of distributed optimization algorithms. Using the Single Program…
arXiv:2511.02776v2 Announce Type: replace
Abstract: Recent progress in large-scale robotic datasets and vision-language models (VLMs) has advanced research on vision-language-action (VLA) models. However, existing VLA models still face two fundamental…
arXiv:2605.15122v1 Announce Type: new
Abstract: Robust state estimation for highly dynamic motion of legged robots remains challenging, especially in dynamic, contact-rich scenarios. Traditional approaches often rely on binary contact states that fail…
arXiv:2603.02115v2 Announce Type: replace-cross
Abstract: General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame-level supervision. While effective for expert…
arXiv:2605.15153v1 Announce Type: cross
Abstract: We present Pelican-Unified 1.0, the first embodied foundation model trained according to the principle of unification. Pelican-Unified 1.0 uses a single VLM as a unified understanding module, mapping s…
arXiv:2605.12625v2 Announce Type: replace
Abstract: Continuous-action policies trained on a single demonstrated trajectory per scene suffer from mode collapse: samples cluster around the demonstrated maneuver and the policy cannot represent semantical…
arXiv:2605.14396v1 Announce Type: cross
Abstract: Autonomous vehicles depend on online HD map construction to perceive lane boundaries, dividers, and pedestrian crossings — safety-critical road elements that directly govern motion planning. While exi…
arXiv:2511.14751v2 Announce Type: replace-cross
Abstract: We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the base model. Co-Me distilled a light-weight c…
arXiv:2605.14526v1 Announce Type: cross
Abstract: Differentiable simulation of soft bodies is a foundation for system identification, trajectory optimization, and Real2Sim transfer. Yet, existing methods such as the differentiable Projective Dynamics …