InternVLA-A15 is a new Vision-Language-Action (VLA) model released by InternRobotics, targeting robotic manipulation and control tasks. The model is accompanied by a Hugging Face collection and an associated technical paper.

Proposes foundation world-action models targeting real-time humanoid loco-manipulation, integrating world modeling with action generation for unified locomotion and manipulation control.

LightDP introduces a framework to accelerate Diffusion Transformer-based policies for real-time, on-device deployment in robot manipulation tasks, achieving efficiency for visuomotor control without sacrificing performance. Accepted to ICCV 2025.

A neural dynamics world model paired with model-free RL for quadruped and humanoid locomotion in IsaacLab, enabling long-horizon autoregressive prediction with policies trained in imagined rollouts that outperform model-based baselines on sim-to-real transfer.

A self-supervised ViT backbone pretrained with masked boundary modeling for dense spatial perception, achieving strong zero-shot results on depth estimation, segmentation, and embodied tasks.

A VLA framework that curates experience into short- and long-term memory vaults, condenses them to latent tokens, and integrates directly into VLA reasoning, evaluated on SimplerEnv and LIBERO benchmarks.

A native video-action foundation model pretrained from scratch with a semantic visual-action tokenizer and foresight reasoning, enabling real-time robot control at ≤150 Hz on consumer GPUs without relying on retrofitted VLMs or video generators.

A large-scale world model trained on 12,980 hours of data and benchmarked across 324,000+ simulated rollouts, designed for comprehensive robot policy evaluation.

Proposes a unified RL policy trained in simulation that jointly handles force and position control without force sensors, enabling compliant behaviors and improved force-aware imitation learning on quadrupedal and humanoid robots.

A single vision-language model that unifies high-level reasoning, long-horizon planning, and human-robot interaction, serving as a cognitive layer above low-level vision-language-action models.