vision-language-action

DyPES-VLA is a cross-embodiment VLA model that learns shared dynamics priors via future prediction and uses an embodiment-specific Mixture-of-Experts action head to control robots in their native action spaces, achieving state-of-the-art results on LIBERO, RoboCasa, and RoboTwin benchmarks.

Xiaomi released XR-1, a robot foundation model trained on over 100K hours of real-world manipulation trajectories. Built on Qwen3-VL and a Diffusion Transformer, it enables out-of-the-box mobile manipulation in unseen environments.

Introduces W2-VLA, a vision-language-action model for fine-grained robot manipulation that models task-conditioned future wrist observations, achieving improved manipulation performance on benchmarks and real-world tasks.

BridgeVLA++ is a memory-augmented vision-language-action framework for 3D robot manipulation that builds on BridgeVLA to add spatio-temporal memory, achieving state-of-the-art results on memory-dependent manipulation benchmarks while preserving data efficiency and generalization.

This paper introduces DRIFT, an adversarial patch attack targeting flow-matching vision-language-action models like pi0 and pi0.5, showing that prior robustness claims are illusory and that attacking only the first denoising step is both stronger and cheaper, breaking nearly all solvable tasks in LIBERO suites.

Ego2Robot is a scalable pipeline that converts egocentric human manipulation videos into robot training data via action retargeting and visual synthesis, producing 18,561 hours of data across 15 robot morphologies. Experiments show that joint pretraining on this synthesized data improves out-of-distribution generalization for vision-language-action models, including on real-robot deployment.

Introduces WCM, a World Critic Model that jointly predicts future latent states and estimates values to improve temporal modeling for Vision-Language-Action reinforcement learning, achieving state-of-the-art results across robotic manipulation benchmarks.

DeepMind introduces Gemini Robotics 2, a set of AI models (VLA, ER, On-Device) that enable robots to perform whole-body control, fine dexterity, and multi-robot collaboration, with local on-device execution and fast adaptation to new robot bodies.

This paper introduces RL^2, an adaptive inference-time steering framework for Vision-Language-Action models that uses offline RL on latent representations to compose action flows, activating steering only when failure is predicted. It achieves up to +17.3% success rate improvements on SIMPLER and PolaRiS benchmarks and demonstrates real-world transfer.

TurboVLA introduces a new Vision-Language-Action paradigm that directly maps vision and language to action, achieving 97.7% success on LIBERO with only 0.2B parameters and real-time inference at 32 Hz on consumer GPUs, significantly reducing computational cost.

ReferTrack introduces a referring-then-tracking paradigm for embodied visual tracking, achieving state-of-the-art performance on EVT-Bench with single-view success rates up to 89.4%, and demonstrating robust sim-to-real transfer on legged and humanoid robots.

OpenBMB releases MiniCPM-RobotTrack, a compact vision-language-action model built on MiniCPM4-0.5B for natural-language-conditioned target tracking, achieving 5+ FPS on unitree Go2, with quality-driven self-evolving data and efficient onboard inference.

OpenBMB releases MiniCPM-RobotManip, a 1.5B vision-language-action model for on-device robot manipulation that outperforms larger models with efficient streaming inference and long-horizon visual memory.

JoyNexus proposes a unified service for multi-tenant VLA model post-training, decoupling training, inference, and environment services with shared base models and tenant-specific slots, improving efficiency through group batching and scheduling.

This paper introduces UESF-Bench, a large-scale benchmark for unified embodied human seeking and following, and proposes SeekFollow-VLA, a vision-language-action framework that handles semantic-guided exploration and reliable behavior switching between searching and following.

Xiaomi introduces Xiaomi-Robotics-1, a vision-language-action foundation model trained on over 100,000 hours of real-world manipulation trajectories, demonstrating clear scaling laws and achieving high success rates on real-world tasks with minimal fine-tuning data.

A new dedicated Robotics page on Papers with Code aggregates major benchmarks, trending papers with linked code, and open-source artifacts, tracking progress over time across benchmarks like LIBERO and SimplerEnv.

Introduces Learning from Hindsight (LfH), a method that applies hindsight relabeling to RL post-training of vision-language-action models. By relabeling failed robot rollouts with the tasks they actually achieved, LfH achieves 5x improvement in sample efficiency on out-of-distribution manipulation tasks.

CLAP proposes a method to convert pretrained vision-language models (VLMs) into vision-language-action models (VLAs) by prepending natural-language action descriptions to action token sequences, preserving semantic capabilities without architectural changes. It achieves 90.8% on LIBERO and improves robustness.

This paper introduces robot-centric pointmaps, which encode 3D scene coordinates in the robot frame directly into image pixels, to resolve the frame mismatch between camera observations and robot action definitions in Vision-Language-Action models. The method improves Pi0.5 and SmolVLA on RoboCasa and generalizes better to unseen camera placements in real-robot experiments.