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Vision-Language-Action (VLA) models have emerged as a powerful framework that unifies perception, language, and control, enabling robots to perform diverse tasks through multimodal understanding. However, current VLA models typically contain massive parameters and rely heavily on large-scale robot data pretraining, leading to high computational costs during training, as well as limited deployability for real-time inference. Moreover, most training paradigms often degrade the perceptual representations of the vision-language backbone, resulting in overfitting and poor generalization to downstream tasks. In this work, we present Evo-1, a lightweight VLA model that reduces computation and improves deployment efficiency, while maintaining strong performance without pretraining on robot data. Evo-1 builds on a native multimodal Vision-Language model (VLM), incorporating a novel cross-modulated diffusion transformer along with an optimized integration module, together forming an effective architecture. We further introduce a two-stage training paradigm that progressively aligns action with perception, preserving the representations of the VLM. Notably, with only 0.77 billion parameters, Evo-1 achieves state-of-the-art results on the Meta-World and RoboTwin suite, surpassing the previous best models by 12.4% and 6.9%, respectively, and also attains a competitive result of 94.8% on LIBERO. In real-world evaluations, Evo-1 attains a 78% success rate with high inference frequency and low memory overhead, outperforming all baseline methods. We release code, data, and model weights to facilitate future research on lightweight and efficient VLA models.

Vision-Language-Action models can struggle when manipulation requires precise spatial understanding from RGB observations alone. Evo-Depth introduces a lightweight depth-enhanced VLA framework that improves spatial grounding without extra sensing hardware. It extracts compact implicit depth cues from multi-view RGB inputs, injects them into vision-language representations through depth-aware spatial enhancement, and aligns the resulting features with action learning through progressive training. With a compact 0.9B-parameter model, Evo-Depth reports strong results across simulation benchmarks and real-world experiments while maintaining low memory use and fast inference.

Vision-Language-Action (VLA) models have emerged as a promising framework for enabling generalist robots capable of perceiving, reasoning, and acting in the real world. These models usually build upon pretrained Vision-Language Models (VLMs), which excel at semantic understanding due to large-scale image and text pretraining. However, existing VLMs typically lack precise spatial understanding capabilities, as they are primarily tuned on 2D image-text pairs without 3D supervision. To address this limitation, recent approaches have incorporated explicit 3D inputs such as point clouds or depth maps, but this necessitates additional depth sensors or pre-trained depth estimation models, which may yield defective results. In contrast, our work introduces a plug-and-play module that implicitly incorporates 3D geometry features into VLA models by leveraging an off-the-shelf visual geometry foundation model. This integration provides the model with depth-aware visual representations, improving its ability to understand the geometric structure of the scene and the spatial relationships among objects from RGB images alone. We evaluate our method on a set of spatially challenging tasks in both simulation and the real world. Extensive evaluations show that our method significantly improves the performance of state-of-the-art VLA models across diverse scenarios.

This article considers the implementation of an observer-based logarithmic control scheme for physical human–robot interaction, which is a typical Lagrangian system. The novelty lies in using a switching term in the logarithmic sliding mode observer to describe the operator’s behavior without any sensors, and applying adaptive parameters in the logarithmic sliding mode controller (SMC) to practically stabilize reaching the sliding surface using a chattering-free nonsingular reaching law in finite time. A reset mechanism is synthesized into the control system to enhance the transient response. The motion on the sliding surface is analyzed from the perspective of practical finite-time stability, from which both the convergence regions of the tracking and estimate errors can be determined. The numerical and experimental results verify the effectiveness and advantage of the proposed reset logarithmic SMC and observer for human–robot interaction compared to the existing linear SMC and terminal SMC. With regards to settling time and rising time, the superiority of transient performance in experimental results is coincident with the stability analysis.